mBio. 2021 Mar 9;12(2):e03457-20. doi: 10.1128/mBio.03457-20.

ABSTRACT

Cryptococcus neoformans is a ubiquitous, opportunistic fungal pathogen that kills almost 200,000 people worldwide each year. It is acquired when mammalian hosts inhale the infectious propagules; these are deposited in the lung and, in the context of immunocompromise, may disseminate to the brain and cause lethal meningoencephalitis. Once inside the host, C. neoformans undergoes a variety of adaptive processes, including secretion of virulence factors, expansion of a polysaccharide capsule that impedes phagocytosis, and the production of giant (Titan) cells. The transcription factor Pdr802 is one regulator of these responses to the host environment. Expression of the corresponding gene is highly induced under host-like conditions in vitro and is critical for C. neoformans dissemination and virulence in a mouse model of infection. Direct targets of Pdr802 include the quorum sensing proteins Pqp1, Opt1, and Liv3; the transcription factors Stb4, Zfc3, and Bzp4, which regulate cryptococcal brain infectivity and capsule thickness; the calcineurin targets Had1 and Crz1, important for cell wall remodeling and C. neoformans virulence; and additional genes related to resistance to host temperature and oxidative stress, and to urease activity. Notably, cryptococci engineered to lack Pdr802 showed a dramatic increase in Titan cells, which are not phagocytosed and have diminished ability to directly cross biological barriers. This explains the limited dissemination of pdr802 mutant cells to the central nervous system and the consequently reduced virulence of this strain. The role of Pdr802 as a negative regulator of Titan cell formation is thus critical for cryptococcal pathogenicity.IMPORTANCE The pathogenic yeast Cryptococcus neoformans presents a worldwide threat to human health, especially in the context of immunocompromise, and current antifungal therapy is hindered by cost, limited availability, and inadequate efficacy. After the infectious particle is inhaled, C. neoformans initiates a complex transcriptional program that integrates cellular responses and enables adaptation to the host lung environment. Here, we describe the role of the transcription factor Pdr802 in the response to host conditions and its impact on C. neoformans virulence. We identified direct targets of Pdr802 and also discovered that it regulates cellular features that influence movement of this pathogen from the lung to the brain, where it causes fatal disease. These findings significantly advance our understanding of a serious disease.

PMID:33688010 | PMC:PMC8092302 | DOI:10.1128/mBio.03457-20

Bioinformatics. 2021 Jun 9;37(9):1234-1245. doi: 10.1093/bioinformatics/btaa947.

ABSTRACT

MOTIVATION: The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now.

RESULTS: We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2.

AVAILABILITY AND IMPLEMENTATION: Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:33135076 | PMC:PMC8189679 | DOI:10.1093/bioinformatics/btaa947

Genome Res. 2020 Mar;30(3):459-471. doi: 10.1101/gr.259655.119. Epub 2020 Feb 14.

ABSTRACT

A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.

PMID:32060051 | PMC:PMC7111528 | DOI:10.1101/gr.259655.119

Genetics. 2018 Apr;208(4):1643-1656. doi: 10.1534/genetics.118.300770. Epub 2018 Feb 27.

ABSTRACT

Insulin resistance is associated with obesity, cardiovascular disease, non-alcoholic fatty liver disease, and type 2 diabetes. These complications are exacerbated by a high-calorie diet, which we used to model type 2 diabetes in Drosophila melanogaster Our studies focused on the fat body, an adipose- and liver-like tissue that stores fat and maintains circulating glucose. A gene regulatory network was constructed to predict potential regulators of insulin signaling in this tissue. Genomic characterization of fat bodies suggested a central role for the transcription factor Seven-up (Svp). Here, we describe a new role for Svp as a positive regulator of insulin signaling. Tissue-specific loss-of-function showed that Svp is required in the fat body to promote glucose clearance, lipid turnover, and insulin signaling. Svp appears to promote insulin signaling, at least in part, by inhibiting ecdysone signaling. Svp also impairs the immune response possibly via inhibition of antimicrobial peptide expression in the fat body. Taken together, these studies show that gene regulatory networks can help identify positive regulators of insulin signaling and metabolic homeostasis using the Drosophila fat body.

PMID:29487137 | PMC:PMC5887154 | DOI:10.1534/genetics.118.300770

G3 (Bethesda). 2018 Mar 2;8(3):815-822. doi: 10.1534/g3.117.300357.

ABSTRACT

Received wisdom in the field of fungal biology holds that the process of editing a genome by transformation and homologous recombination is inherently mutagenic. However, that belief is based on circumstantial evidence. We provide the first direct measurement of the effects of transformation on a fungal genome by sequencing the genomes of 29 transformants and 30 untransformed controls with high coverage. Contrary to the received wisdom, our results show that transformation of DNA segments flanked by long targeting sequences, followed by homologous recombination and selection for a drug marker, is extremely safe. If a transformation deletes a gene, that may create selective pressure for a few compensatory mutations, but even when we deleted a gene, we found fewer than two point mutations per deletion strain, on average. We also tested these strains for changes in gene expression and found only a few genes that were consistently differentially expressed between the wild type and strains modified by genomic insertion of a drug resistance marker. As part of our report, we provide the assembled genome sequence of the commonly used laboratory strain Cryptococcus neoformans var. grubii strain KN99α.

PMID:29305388 | PMC:PMC5844303 | DOI:10.1534/g3.117.300357

Bioinformatics. 2018 Jan 15;34(2):249-257. doi: 10.1093/bioinformatics/btx563.

ABSTRACT

MOTIVATION: Cells process information, in part, through transcription factor (TF) networks, which control the rates at which individual genes produce their products. A TF network map is a graph that indicates which TFs bind and directly regulate each gene. Previous work has described network mapping algorithms that rely exclusively on gene expression data and 'integrative' algorithms that exploit a wide range of data sources including chromatin immunoprecipitation sequencing (ChIP-seq) of many TFs, genome-wide chromatin marks, and binding specificities for many TFs determined in vitro. However, such resources are available only for a few major model systems and cannot be easily replicated for new organisms or cell types.

RESULTS: We present NetProphet 2.0, a 'data light' algorithm for TF network mapping, and show that it is more accurate at identifying direct targets of TFs than other, similarly data light algorithms. In particular, it improves on the accuracy of NetProphet 1.0, which used only gene expression data, by exploiting three principles. First, combining multiple approaches to network mapping from expression data can improve accuracy relative to the constituent approaches. Second, TFs with similar DNA binding domains bind similar sets of target genes. Third, even a noisy, preliminary network map can be used to infer DNA binding specificities from promoter sequences and these inferred specificities can be used to further improve the accuracy of the network map.

AVAILABILITY AND IMPLEMENTATION: Source code and comprehensive documentation are freely available at https://github.com/yiming-kang/NetProphet_2.0.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:28968736 | PMC:PMC5860202 | DOI:10.1093/bioinformatics/btx563

Proc Natl Acad Sci U S A. 2016 Nov 22;113(47):E7428-E7437. doi: 10.1073/pnas.1603577113. Epub 2016 Nov 3.

ABSTRACT

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.

PMID:27810962 | PMC:PMC5127345 | DOI:10.1073/pnas.1603577113

Trends Genet. 2016 Nov;32(11):736-750. doi: 10.1016/j.tig.2016.08.009. Epub 2016 Oct 6.

ABSTRACT

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

PMID:27720190 | PMC:PMC5117949 | DOI:10.1016/j.tig.2016.08.009

Mol Cell. 2016 Oct 6;64(1):213. doi: 10.1016/j.molcel.2016.09.035.

NO ABSTRACT

PMID:27716485 | DOI:10.1016/j.molcel.2016.09.035

mBio. 2016 Apr 19;7(2):e00313-16. doi: 10.1128/mBio.00313-16.

ABSTRACT

Cryptococcus neoformans is a ubiquitous, opportunistic fungal pathogen that kills over 600,000 people annually. Here, we report integrated computational and experimental investigations of the role and mechanisms of transcriptional regulation in cryptococcal infection. Major cryptococcal virulence traits include melanin production and the development of a large polysaccharide capsule upon host entry; shed capsule polysaccharides also impair host defenses. We found that both transcription and translation are required for capsule growth and that Usv101 is a master regulator of pathogenesis, regulating melanin production, capsule growth, and capsule shedding. It does this by directly regulating genes encoding glycoactive enzymes and genes encoding three other transcription factors that are essential for capsule growth: GAT201, RIM101, and SP1. Murine infection with cryptococci lacking Usv101 significantly alters the kinetics and pathogenesis of disease, with extended survival and, unexpectedly, death by pneumonia rather than meningitis. Our approaches and findings will inform studies of other pathogenic microbes.

IMPORTANCE: Cryptococcus neoformans causes fatal meningitis in immunocompromised individuals, mainly HIV positive, killing over 600,000 each year. A unique feature of this yeast, which makes it particularly virulent, is its polysaccharide capsule; this structure impedes host efforts to combat infection. Capsule size and structure respond to environmental conditions, such as those encountered in an infected host. We have combined computational and experimental tools to elucidate capsule regulation, which we show primarily occurs at the transcriptional level. We also demonstrate that loss of a novel transcription factor alters virulence factor expression and host cell interactions, changing the lethal condition from meningitis to pneumonia with an exacerbated host response. We further demonstrate the relevant targets of regulation and kinetically map key regulatory and host interactions. Our work elucidates mechanisms of capsule regulation, provides methods and resources to the research community, and demonstrates an altered pathogenic outcome that resembles some human conditions.

PMID:27094327 | PMC:PMC4850258 | DOI:10.1128/mBio.00313-16

Mol Cell. 2015 Aug 20;59(4):685-97. doi: 10.1016/j.molcel.2015.07.008. Epub 2015 Aug 6.

ABSTRACT

We developed Split DamID (SpDamID), a protein complementation version of DamID, to mark genomic DNA bound in vivo by interacting or juxtapositioned transcription factors. Inactive halves of DAM (DNA adenine methyltransferase) were fused to protein pairs to be queried. Either direct interaction between proteins or proximity enabled DAM reconstitution and methylation of adenine in GATC. Inducible SpDamID was used to analyze Notch-mediated transcriptional activation. We demonstrate that Notch complexes label RBP sites broadly across the genome and show that a subset of these complexes that recruit MAML and p300 undergo changes in chromatin accessibility in response to Notch signaling. SpDamID differentiates between monomeric and dimeric binding, thereby allowing for identification of half-site motifs used by Notch dimers. Motif enrichment of Notch enhancers coupled with SpDamID reveals co-targeting of regulatory sequences by Notch and Runx1. SpDamID represents a sensitive and powerful tool that enables dynamic analysis of combinatorial protein-DNA transactions at a genome-wide level.

PMID:26257285 | PMC:PMC4553207 | DOI:10.1016/j.molcel.2015.07.008

Genome Res. 2015 May;25(5):690-700. doi: 10.1101/gr.184101.114. Epub 2015 Feb 2.

ABSTRACT

Key steps in understanding a biological process include identifying genes that are involved and determining how they are regulated. We developed a novel method for identifying transcription factors (TFs) involved in a specific process and used it to map regulation of the key virulence factor of a deadly fungus-its capsule. The map, built from expression profiles of 41 TF mutants, includes 20 TFs not previously known to regulate virulence attributes. It also reveals a hierarchy comprising executive, midlevel, and "foreman" TFs. When grouped by temporal expression pattern, these TFs explain much of the transcriptional dynamics of capsule induction. Phenotypic analysis of TF deletion mutants revealed complex relationships among virulence factors and virulence in mice. These resources and analyses provide the first integrated, systems-level view of capsule regulation and biosynthesis. Our methods dramatically improve the efficiency with which transcriptional networks can be analyzed, making genomic approaches accessible to laboratories focused on specific physiological processes.

PMID:25644834 | PMC:PMC4417117 | DOI:10.1101/gr.184101.114

Eukaryot Cell. 2014 Jun;13(6):832-42. doi: 10.1128/EC.00054-14. Epub 2014 Apr 18.

ABSTRACT

Cryptococcus neoformans is an opportunistic yeast responsible for lethal meningoencephalitis in humans. This pathogen elaborates a polysaccharide capsule, which is its major virulence factor. Mannose constitutes over one-half of the capsule mass and is also extensively utilized in cell wall synthesis and in glycosylation of proteins and lipids. The activated mannose donor for most biosynthetic reactions, GDP-mannose, is made in the cytosol, although it is primarily consumed in secretory organelles. This compartmentalization necessitates specific transmembrane transporters to make the donor available for glycan synthesis. We previously identified two cryptococcal GDP-mannose transporters, Gmt1 and Gmt2. Biochemical studies of each protein expressed in Saccharomyces cerevisiae showed that both are functional, with similar kinetics and substrate specificities in vitro. We have now examined these proteins in vivo and demonstrate that cells lacking Gmt1 show significant phenotypic differences from those lacking Gmt2 in terms of growth, colony morphology, protein glycosylation, and capsule phenotypes. Some of these observations may be explained by differential expression of the two genes, but others suggest that the two proteins play overlapping but nonidentical roles in cryptococcal biology. Furthermore, gmt1 gmt2 double mutant cells, which are unexpectedly viable, exhibit severe defects in capsule synthesis and protein glycosylation and are avirulent in mouse models of cryptococcosis.

PMID:24747214 | PMC:PMC4054277 | DOI:10.1128/EC.00054-14

Genome Res. 2013 Aug;23(8):1319-28. doi: 10.1101/gr.150904.112. Epub 2013 May 1.

ABSTRACT

A critical step in understanding how a genome functions is determining which transcription factors (TFs) regulate each gene. Accordingly, extensive effort has been devoted to mapping TF networks. In Saccharomyces cerevisiae, protein-DNA interactions have been identified for most TFs by ChIP-chip, and expression profiling has been done on strains deleted for most TFs. These studies revealed that there is little overlap between the genes whose promoters are bound by a TF and those whose expression changes when the TF is deleted, leaving us without a definitive TF network for any eukaryote and without an efficient method for mapping functional TF networks. This paper describes NetProphet, a novel algorithm that improves the efficiency of network mapping from gene expression data. NetProphet exploits a fundamental observation about the nature of TF networks: The response to disrupting or overexpressing a TF is strongest on its direct targets and dissipates rapidly as it propagates through the network. Using S. cerevisiae data, we show that NetProphet can predict thousands of direct, functional regulatory interactions, using only gene expression data. The targets that NetProphet predicts for a TF are at least as likely to have sites matching the TF's binding specificity as the targets implicated by ChIP. Unlike most ChIP targets, the NetProphet targets also show evidence of functional regulation. This suggests a surprising conclusion: The best way to begin mapping direct, functional TF-promoter interactions may not be by measuring binding. We also show that NetProphet yields new insights into the functions of several yeast TFs, including a well-studied TF, Cbf1, and a completely unstudied TF, Eds1.

PMID:23636944 | PMC:PMC3730105 | DOI:10.1101/gr.150904.112

J Biol Chem. 2013 Mar 22;288(12):8028-8042. doi: 10.1074/jbc.M112.371047. Epub 2013 Jan 25.

ABSTRACT

The Drosophila fat body is a liver- and adipose-like tissue that stores fat and serves as a detoxifying and immune responsive organ. We have previously shown that a high sugar diet leads to elevated hemolymph glucose and systemic insulin resistance in developing larvae and adults. Here, we used stable isotope tracer feeding to demonstrate that rearing larvae on high sugar diets impaired the synthesis of esterified fatty acids from dietary glucose. Fat body lipid profiling revealed changes in both carbon chain length and degree of unsaturation of fatty acid substituents, particularly in stored triglycerides. We tested the role of the fat body in larval tolerance of caloric excess. Our experiments demonstrated that lipogenesis was necessary for animals to tolerate high sugar feeding as tissue-specific loss of orthologs of carbohydrate response element-binding protein or stearoyl-CoA desaturase 1 resulted in lethality on high sugar diets. By contrast, increasing the fat content of the fat body by knockdown of king-tubby was associated with reduced hyperglycemia and improved growth and tolerance of high sugar diets. Our work supports a critical role for the fat body and the Drosophila carbohydrate response element-binding protein ortholog in metabolic homeostasis in Drosophila.

PMID:23355467 | PMC:PMC3605622 | DOI:10.1074/jbc.M112.371047

Mol Cancer Res. 2012 Mar;10(3):316-25. doi: 10.1158/1541-7786.MCR-11-0520. Epub 2012 Jan 17.

ABSTRACT

DICER1 is essential for the generation of mature miRNAs and other short noncoding RNAs. Several lines of investigation implicate DICER1 as a tumor suppressor. Reduced DICER1 levels and changes in miRNA abundance have been associated with aggressive tumor phenotypes. The global effects of reduced DICER1 on mRNA transcript abundance in tumor cells remain largely unknown. We used short hairpin RNA to stably knock down DICER1 in endometrial cancer cell lines to begin to determine how reduced DICER1 activity contributes to tumor phenotypes. DICER1 knockdown did not affect cell proliferation but caused enhanced cell migration and growth in soft agar. miRNA and mRNA profiling in KLE cells revealed overall decreases in miRNA levels and changes in the relative abundance of many mRNAs. One of the most striking changes in mRNA levels was the upregulation of IFN-stimulated genes (ISG), the majority of which lack known miRNA target sequences. IFNβ, a key upstream regulator of the IFN response, was significantly increased in DICER1 knockdowns in the AN3CA, Ishikawa, and KLE endometrial cancer cell lines and in the normal endometrial cell line EM-E6/E7/TERT. IFNβ secreted in media from KLE and EM-E6/E7/TERT shDcr cells was sufficient to activate an IFN response in HT29 cells. The reduced miRNA processing in DICER1 knockdowns was associated with increases in pre-miRNAs in the cytoplasm. Our findings suggest that elevated pre-miRNA levels trigger the IFN response to double-stranded RNA. We thus report a novel effect of reduced DICER1 function in cancer cells.

PMID:22252463 | PMC:PMC3307918 | DOI:10.1158/1541-7786.MCR-11-0520

PLoS Pathog. 2011 Dec;7(12):e1002411. doi: 10.1371/journal.ppat.1002411. Epub 2011 Dec 8.

ABSTRACT

Cryptococcus neoformans is an opportunistic fungal pathogen that causes serious human disease in immunocompromised populations. Its polysaccharide capsule is a key virulence factor which is regulated in response to growth conditions, becoming enlarged in the context of infection. We used microarray analysis of cells stimulated to form capsule over a range of growth conditions to identify a transcriptional signature associated with capsule enlargement. The signature contains 880 genes, is enriched for genes encoding known capsule regulators, and includes many uncharacterized sequences. One uncharacterized sequence encodes a novel regulator of capsule and of fungal virulence. This factor is a homolog of the yeast protein Ada2, a member of the Spt-Ada-Gcn5 Acetyltransferase (SAGA) complex that regulates transcription of stress response genes via histone acetylation. Consistent with this homology, the C. neoformans null mutant exhibits reduced histone H3 lysine 9 acetylation. It is also defective in response to a variety of stress conditions, demonstrating phenotypes that overlap with, but are not identical to, those of other fungi with altered SAGA complexes. The mutant also exhibits significant defects in sexual development and virulence. To establish the role of Ada2 in the broader network of capsule regulation we performed RNA-Seq on strains lacking either Ada2 or one of two other capsule regulators: Cir1 and Nrg1. Analysis of the results suggested that Ada2 functions downstream of both Cir1 and Nrg1 via components of the high osmolarity glycerol (HOG) pathway. To identify direct targets of Ada2, we performed ChIP-Seq analysis of histone acetylation in the Ada2 null mutant. These studies supported the role of Ada2 in the direct regulation of capsule and mating responses and suggested that it may also play a direct role in regulating capsule-independent antiphagocytic virulence factors. These results validate our experimental approach to dissecting capsule regulation and provide multiple targets for future investigation.

PMID:22174677 | PMC:PMC3234223 | DOI:10.1371/journal.ppat.1002411

Nature. 2011 Mar 24;471(7339):473-9. doi: 10.1038/nature09715. Epub 2010 Dec 22.

ABSTRACT

Drosophila melanogaster is one of the most well studied genetic model organisms; nonetheless, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites. Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. Here we used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development.

PMID:21179090 | PMC:PMC3075879 | DOI:10.1038/nature09715

Science. 2010 Dec 24;330(6012):1787-97. doi: 10.1126/science.1198374. Epub 2010 Dec 22.

ABSTRACT

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.

PMID:21177974 | PMC:PMC3192495 | DOI:10.1126/science.1198374

Genome Res. 2011 Feb;21(2):301-14. doi: 10.1101/gr.112961.110. Epub 2010 Dec 22.

ABSTRACT

Drosophila melanogaster cell lines are important resources for cell biologists. Here, we catalog the expression of exons, genes, and unannotated transcriptional signals for 25 lines. Unannotated transcription is substantial (typically 19% of euchromatic signal). Conservatively, we identify 1405 novel transcribed regions; 684 of these appear to be new exons of neighboring, often distant, genes. Sixty-four percent of genes are expressed detectably in at least one line, but only 21% are detected in all lines. Each cell line expresses, on average, 5885 genes, including a common set of 3109. Expression levels vary over several orders of magnitude. Major signaling pathways are well represented: most differentiation pathways are "off" and survival/growth pathways "on." Roughly 50% of the genes expressed by each line are not part of the common set, and these show considerable individuality. Thirty-one percent are expressed at a higher level in at least one cell line than in any single developmental stage, suggesting that each line is enriched for genes characteristic of small sets of cells. Most remarkable is that imaginal disc-derived lines can generally be assigned, on the basis of expression, to small territories within developing discs. These mappings reveal unexpected stability of even fine-grained spatial determination. No two cell lines show identical transcription factor expression. We conclude that each line has retained features of an individual founder cell superimposed on a common "cell line" gene expression pattern.

PMID:21177962 | PMC:PMC3032933 | DOI:10.1101/gr.112961.110

Proc Natl Acad Sci U S A. 2010 Sep 21;107(38):16743-8. doi: 10.1073/pnas.0912483107. Epub 2010 Sep 1.

ABSTRACT

The ability to design and engineer organisms demands the ability to predict kinetic responses of novel regulatory networks built from well-characterized biological components. Surprisingly, few validated kinetic models of complex regulatory networks have been derived by combining models of the network components. A major bottleneck in producing such models is the difficulty of measuring in vivo rate constants for components of complex networks. We demonstrate that a simple, genetic approach to measuring rate constants in vivo produces an accurate kinetic model of the complex network that Saccharomyces cerevisiae employs to regulate the expression of genes encoding glucose transporters. The model predicts a transient pulse of transcription of HXT4 (but not HXT2 or HXT3) in response to addition of a small amount of glucose to cells, an outcome we observed experimentally. Our model also provides a mechanistic explanation for this result: HXT2-4 are governed by a type 2, incoherent feed forward regulatory loop involving the Rgt1 and Mig2 transcriptional repressors. The efficiency with which Rgt1 and Mig2 repress expression of each HXT gene determines which of them have a pulse of transcription in response to glucose. Finally, the model correctly predicts how lesions in the feed forward loop change the kinetics of induction of HXT4 expression.

PMID:20810924 | PMC:PMC2944743 | DOI:10.1073/pnas.0912483107

Bioinformatics. 2009 Jul 1;25(13):1587-93. doi: 10.1093/bioinformatics/btp273. Epub 2009 May 4.

ABSTRACT

MOTIVATION: The most accurate way to determine the intron-exon structures in a genome is to align spliced cDNA sequences to the genome. Thus, cDNA-to-genome alignment programs are a key component of most annotation pipelines. The scoring system used to choose the best alignment is a primary determinant of alignment accuracy, while heuristics that prevent consideration of certain alignments are a primary determinant of runtime and memory usage. Both accuracy and speed are important considerations in choosing an alignment algorithm, but scoring systems have received much less attention than heuristics.

RESULTS: We present Pairagon, a pair hidden Markov model based cDNA-to-genome alignment program, as the most accurate aligner for sequences with high- and low-identity levels. We conducted a series of experiments testing alignment accuracy with varying sequence identity. We first created 'perfect' simulated cDNA sequences by splicing the sequences of exons in the reference genome sequences of fly and human. The complete reference genome sequences were then mutated to various degrees using a realistic mutation simulator and the perfect cDNAs were aligned to them using Pairagon and 12 other aligners. To validate these results with natural sequences, we performed cross-species alignment using orthologous transcripts from human, mouse and rat. We found that aligner accuracy is heavily dependent on sequence identity. For sequences with 100% identity, Pairagon achieved accuracy levels of >99.6%, with one quarter of the errors of any other aligner. Furthermore, for human/mouse alignments, which are only 85% identical, Pairagon achieved 87% accuracy, higher than any other aligner.

AVAILABILITY: Pairagon source and executables are freely available at http://mblab.wustl.edu/software/pairagon/

PMID:19414532 | PMC:PMC2732315 | DOI:10.1093/bioinformatics/btp273

Bioinformatics. 2009 Mar 15;25(6):801-7. doi: 10.1093/bioinformatics/btp068. Epub 2009 Feb 2.

ABSTRACT

MOTIVATION: Over the past decade, the prospect of inferring networks of gene regulation from high-throughput experimental data has received a great deal of attention. In contrast to the massive effort that has gone into automated deconvolution of biological networks, relatively little effort has been invested in benchmarking the proposed algorithms. The rate at which new network inference methods are being proposed far outpaces our ability to objectively evaluate and compare them. This is largely due to a lack of fully understood biological networks to use as gold standards.

RESULTS: We have developed the most realistic system to date that generates synthetic regulatory networks for benchmarking reconstruction algorithms. The improved biological realism of our benchmark leads to conclusions about the relative accuracies of reconstruction algorithms that are significantly different from those obtained with A-BIOCHEM, an established in silico benchmark.

AVAILABILITY: The synthetic benchmark utility and the specific benchmark networks that were used in our analyses are available at http://mblab.wustl.edu/software/grendel/.

PMID:19188190 | PMC:PMC2732301 | DOI:10.1093/bioinformatics/btp068

Curr Protoc Bioinformatics. 2007 Dec;Chapter 4:Unit 4.8. doi: 10.1002/0471250953.bi0408s20.

ABSTRACT

N-SCAN is a gene-prediction system that combines the methods of ab initio predictors like GENSCAN with information derived from genome comparison. It is the latest in the TWINSCAN series of programs. This unit describes the use of N-SCAN to identify gene structures in eukaryotic genomic sequences. Protocols for using N-SCAN through its Web interface and from the command line in a Linux environment are provided. Detailed discussion about the appropriate parameter settings, input-sequence processing, and choice of genome for comparison are included.

PMID:18428682 | DOI:10.1002/0471250953.bi0408s20

Nat Rev Genet. 2008 Jan;9(1):62-73. doi: 10.1038/nrg2220.

ABSTRACT

The sequencing of large, complex genomes has become routine, but understanding how sequences relate to biological function is less straightforward. Although much attention is focused on how to annotate genomic features such as developmental enhancers and non-coding RNAs, there is still no higher eukaryote for which we know the correct exon-intron structure of at least one ORF for each gene. Despite this uncomfortable truth, genome annotation has made remarkable progress since the first drafts of the human genome were analysed. By combining several computational and experimental methods, we are now closer to producing complete and accurate gene catalogues than ever before.

PMID:18087260 | DOI:10.1038/nrg2220

Nature. 2007 Nov 8;450(7167):203-18. doi: 10.1038/nature06341.

ABSTRACT

Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

PMID:17994087 | DOI:10.1038/nature06341

Genome Res. 2007 Dec;17(12):1763-73. doi: 10.1101/gr.7128207. Epub 2007 Nov 7.

ABSTRACT

A complete and accurate set of human protein-coding gene annotations is perhaps the single most important resource for genomic research after the human-genome sequence itself, yet the major gene catalogs remain incomplete and imperfect. Here we describe a genome-wide effort, carried out as part of the Mammalian Gene Collection (MGC) project, to identify human genes not yet in the gene catalogs. Our approach was to produce gene predictions by algorithms that rely on comparative sequence data but do not require direct cDNA evidence, then to test predicted novel genes by RT-PCR. We have identified 734 novel gene fragments (NGFs) containing 2188 exons with, at most, weak prior cDNA support. These NGFs correspond to an estimated 563 distinct genes, of which >160 are completely absent from the major gene catalogs, while hundreds of others represent significant extensions of known genes. The NGFs appear to be predominantly protein-coding genes rather than noncoding RNAs, unlike novel transcribed sequences identified by technologies such as tiling arrays and CAGE. They tend to be expressed at low levels and in a tissue-specific manner, and they are enriched for roles in motor activity, cell adhesion, connective tissue, and central nervous system development. Our results demonstrate that many important genes and gene fragments have been missed by traditional approaches to gene discovery but can be identified by their evolutionary signatures using comparative sequence data. However, they suggest that hundreds-not thousands-of protein-coding genes are completely missing from the current gene catalogs.

PMID:17989246 | PMC:PMC2099585 | DOI:10.1101/gr.7128207

Nat Biotechnol. 2007 Aug;25(8):883-5. doi: 10.1038/nbt0807-883.

NO ABSTRACT

PMID:17687368 | DOI:10.1038/nbt0807-883

Nat Methods. 2007 Aug;4(8):659-64. doi: 10.1038/nmeth1063. Epub 2007 Jun 24.

ABSTRACT

Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing. Both strategies simplify the Y1H pipeline and reduce the cost of protein-DNA interaction identification. We used a Steiner triple system (STS) to create smart pools of 4-25 TFs. Notably, we uniplexed a small number of highly connected TFs to allow efficient assay deconvolution. Both strategies outperform library screens in terms of coverage, confidence and throughput. These versatile strategies can be adapted both to TFs in other systems and, likely, to other biomolecules and assays as well.

PMID:17589517 | DOI:10.1038/nmeth1063

Bioinformatics. 2007 Mar 1;23(5):545-54. doi: 10.1093/bioinformatics/btl659. Epub 2007 Jan 18.

ABSTRACT

MOTIVATION: Hidden Markov models (HMMs) and generalized HMMs been successfully applied to many problems, but the standard Viterbi algorithm for computing the most probable interpretation of an input sequence (known as decoding) requires memory proportional to the length of the sequence, which can be prohibitive. Existing approaches to reducing memory usage either sacrifice optimality or trade increased running time for reduced memory.

RESULTS: We developed two novel decoding algorithms, Treeterbi and Parallel Treeterbi, and implemented them in the TWINSCAN/N-SCAN gene-prediction system. The worst case asymptotic space and time are the same as for standard Viterbi, but in practice, Treeterbi optimally decodes arbitrarily long sequences with generalized HMMs in bounded memory without increasing running time. Parallel Treeterbi uses the same ideas to split optimal decoding across processors, dividing latency to completion by approximately the number of available processors with constant average overhead per processor. Using these algorithms, we were able to optimally decode all human chromosomes with N-SCAN, which increased its accuracy relative to heuristic solutions. We also implemented Treeterbi for Pairagon, our pair HMM based cDNA-to-genome aligner.

AVAILABILITY: The TWINSCAN/N-SCAN/PAIRAGON open source software package is available from http://genes.cse.wustl.edu.

PMID:17237054 | DOI:10.1093/bioinformatics/btl659

Genome Res. 2007 Feb;17(2):212-8. doi: 10.1101/gr.5661407. Epub 2007 Jan 8.

ABSTRACT

Trace Recalling is a novel method for deconvoluting double traces that result from simultaneously sequencing two DNA templates. Trace Recalling identifies up to two bases at each position of such a trace. The resulting ambiguity sequence is aligned to the genome, identifying one template sequence. A second template sequence is then inferred from this alignment. This technique makes possible many exciting biological applications. Here we present two such applications, alternate splice finding and elucidation of multiple insertion sites in a random insertional mutagenesis library. Our results demonstrate that RT-PCR followed by Trace Recalling is a more efficient and cost effective way to find alternate splices than traditional methods. We also present a method for mapping double-insertion events in a random insertional-mutagenesis library.

PMID:17210930 | PMC:PMC1781353 | DOI:10.1101/gr.5661407

Genome Biol. 2006;7 Suppl 1(Suppl 1):S8.1-9. doi: 10.1186/gb-2006-7-s1-s8. Epub 2006 Aug 7.

ABSTRACT

BACKGROUND: As part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus.

RESULTS: MARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted.

CONCLUSION: The MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small.

PMID:16925842 | PMC:PMC1810557 | DOI:10.1186/gb-2006-7-s1-s8

Genome Biol. 2006;7 Suppl 1(Suppl 1):S5.1-10. doi: 10.1186/gb-2006-7-s1-s5. Epub 2006 Aug 7.

ABSTRACT

BACKGROUND: This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci of putative homologs. Trans alignments contain a high proportion of mismatches, gaps, and/or apparently unspliceable introns, compared to alignments of cDNA sequences to their native loci. The Pairagon+N-SCAN_EST pipeline's first stage is Pairagon, a cDNA-to-genome alignment program based on a PairHMM probability model. This model relies on prior knowledge, such as the fact that introns must begin with GT, GC, or AT and end with AG or AC. It produces very precise alignments of high quality cDNA sequences. In the genomic regions between Pairagon's cDNA alignments, the pipeline combines EST alignments with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST alignments. Because they are based on probability models, both Pairagon and N-SCAN_EST can be trained automatically for new genomes and data sets.

RESULTS: On the ENCODE regions of the human genome, Pairagon+N-SCAN_EST was as accurate as any other system tested in the EGASP assessment, including ENSEMBL and ExoGean.

CONCLUSION: With sufficient mRNA/EST evidence, genome annotation without trans alignments can compete successfully with systems like ENSEMBL and ExoGean, which use trans alignments.

PMID:16925839 | PMC:PMC1810554 | DOI:10.1186/gb-2006-7-s1-s5

Genome Biol. 2006;7 Suppl 1(Suppl 1):S3.1-13. doi: 10.1186/gb-2006-7-s1-s3. Epub 2006 Aug 7.

ABSTRACT

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends.

RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions.

CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment.

PMID:16925837 | PMC:PMC1810552 | DOI:10.1186/gb-2006-7-s1-s3

BMC Bioinformatics. 2006 Jul 3;7:327. doi: 10.1186/1471-2105-7-327.

ABSTRACT

BACKGROUND: ESTs are a tremendous resource for determining the exon-intron structures of genes, but even extensive EST sequencing tends to leave many exons and genes untouched. Gene prediction systems based exclusively on EST alignments miss these exons and genes, leading to poor sensitivity. De novo gene prediction systems, which ignore ESTs in favor of genomic sequence, can predict such "untouched" exons, but they are less accurate when predicting exons to which ESTs align. TWINSCAN is the most accurate de novo gene finder available for nematodes and N-SCAN is the most accurate for mammals, as measured by exact CDS gene prediction and exact exon prediction.

RESULTS: TWINSCAN_EST is a new system that successfully combines EST alignments with TWINSCAN. On the whole C. elegans genome TWINSCAN_EST shows 14% improvement in sensitivity and 13% in specificity in predicting exact gene structures compared to TWINSCAN without EST alignments. Not only are the structures revealed by EST alignments predicted correctly, but these also constrain the predictions without alignments, improving their accuracy. For the human genome, we used the same approach with N-SCAN, creating N-SCAN_EST. On the whole genome, N-SCAN_EST produced a 6% improvement in sensitivity and 1% in specificity of exact gene structure predictions compared to N-SCAN.

CONCLUSION: TWINSCAN_EST and N-SCAN_EST are more accurate than TWINSCAN and N-SCAN, while retaining their ability to discover novel genes to which no ESTs align. Thus, we recommend using the EST versions of these programs to annotate any genome for which EST information is available.TWINSCAN_EST and N-SCAN_EST are part of the TWINSCAN open source software package http://genes.cse.wustl.edu/distribution/download_TS.html.

PMID:16817966 | PMC:PMC1534067 | DOI:10.1186/1471-2105-7-327

Genome Res. 2006 May;16(5):678-85. doi: 10.1101/gr.4766206.

ABSTRACT

Correct gene prediction is impaired by the presence of processed pseudogenes: nonfunctional, intronless copies of real genes found elsewhere in the genome. Gene prediction programs frequently mistake processed pseudogenes for real genes or exons, leading to biologically irrelevant gene predictions. While methods exist to identify processed pseudogenes in genomes, no attempt has been made to integrate pseudogene removal with gene prediction, or even to provide a freestanding tool that identifies such erroneous gene predictions. We have created PPFINDER (for Processed Pseudogene finder), a program that integrates several methods of processed pseudogene finding in mammalian gene annotations. We used PPFINDER to remove pseudogenes from N-SCAN gene predictions, and show that gene prediction improves substantially when gene prediction and pseudogene masking are interleaved. In addition, we used PPFINDER with gene predictions as a parent database, eliminating the need for libraries of known genes. This allows us to run the gene prediction/PPFINDER procedure on newly sequenced genomes for which few genes are known.

PMID:16651666 | PMC:PMC1457044 | DOI:10.1101/gr.4766206

J Comput Biol. 2006 Mar;13(2):379-93. doi: 10.1089/cmb.2006.13.379.

ABSTRACT

The multiple species de novo gene prediction problem can be stated as follows: given an alignment of genomic sequences from two or more organisms, predict the location and structure of all protein-coding genes in one or more of the sequences. Here, we present a new system, N-SCAN (a.k.a. TWINSCAN 3.0), for addressing this problem. N-SCAN can model the phylogenetic relationships between the aligned genome sequences, context dependent substitution rates, and insertions and deletions. An implementation of N-SCAN was created and used to generate predictions for the entire human genome and the genome of the fruit fly Drosophila melanogaster. Analyses of the predictions reveal that N-SCAN's accuracy in both human and fly exceeds that of all previously published whole-genome de novo gene predictors.

PMID:16597247 | DOI:10.1089/cmb.2006.13.379

J Biol Chem. 2006 Apr 21;281(16):11292-300. doi: 10.1074/jbc.M512118200. Epub 2006 Feb 7.

ABSTRACT

We have sequenced 36,641 expressed sequence tags from laser capture microdissected adult mouse gastric and small intestinal epithelial progenitors, obtaining 4031 and 3324 unique transcripts, respectively. Using Gene Ontology (GO) terms, each data set was compared with cDNA libraries from intact adult stomach and small intestine. Genes in GO categories enriched in progenitors were filtered against genes in GO categories represented in hematopoietic, neural, and embryonic stem cell transcriptomes and mapped onto transcription factor networks, plus canonical signal transduction and metabolic pathways. Wnt/beta-catenin, phosphoinositide-3/Akt kinase, insulin-like growth factor-1, vascular endothelial growth factor, integrin, and gamma-aminobutyric acid receptor signaling cascades, plus glycerolipid, fatty acid, and amino acid metabolic pathways are among those prominently represented in adult gut progenitors. The results reveal shared as well as distinctive features of adult gut stem cells when compared with other stem cell populations.

PMID:16464855 | DOI:10.1074/jbc.M512118200

Genome Res. 2005 Dec;15(12):1777-86. doi: 10.1101/gr.3866105.

ABSTRACT

Driven by competition, automation, and technology, the genomics community has far exceeded its ambition to sequence the human genome by 2005. By analyzing mammalian genomes, we have shed light on the history of our DNA sequence, determined that alternatively spliced RNAs and retroposed pseudogenes are incredibly abundant, and glimpsed the apparently huge number of non-coding RNAs that play significant roles in gene regulation. Ultimately, genome science is likely to provide comprehensive catalogs of these elements. However, the methods we have been using for most of the last 10 years will not yield even one complete open reading frame (ORF) for every gene--the first plateau on the long climb toward a comprehensive catalog. These strategies--sequencing randomly selected cDNA clones, aligning protein sequences identified in other organisms, sequencing more genomes, and manual curation--will have to be supplemented by large-scale amplification and sequencing of specific predicted mRNAs. The steady improvements in gene prediction that have occurred over the last 10 years have increased the efficacy of this approach and decreased its cost. In this Perspective, I review the state of gene prediction roughly 10 years ago, summarize the progress that has been made since, argue that the primary ORF identification methods we have relied on so far are inadequate, and recommend a path toward completing the Catalog of Protein Coding Genes, Version 1.0.

PMID:16339376 | DOI:10.1101/gr.3866105

BMC Bioinformatics. 2005 May 30;6:131. doi: 10.1186/1471-2105-6-131.

ABSTRACT

BACKGROUND: Despite the continuous production of genome sequence for a number of organisms, reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularly true for genomes for which there is not a large collection of known gene sequences, such as the recently published chicken genome. We used the chicken sequence to test comparative and homology-based gene-finding methods followed by experimental validation as an effective genome annotation method.

RESULTS: We performed experimental evaluation by RT-PCR of three different computational gene finders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram was computed and each component of it was evaluated. The results showed that de novo comparative methods can identify up to about 700 chicken genes with no previous evidence of expression, and can correctly extend about 40% of homology-based predictions at the 5' end.

CONCLUSIONS: De novo comparative gene prediction followed by experimental verification is effective at enhancing the annotation of the newly sequenced genomes provided by standard homology-based methods.

PMID:15924626 | PMC:PMC1174864 | DOI:10.1186/1471-2105-6-131

Genome Res. 2005 May;15(5):742-7. doi: 10.1101/gr.3696205.

ABSTRACT

The retrainable, comparative gene predictor N-SCAN integrates multigenome modeling and 5' untranslated region (5' UTR) modeling. In this article, we evaluate N-SCAN's transcription-start site (TSS) and first exon predictions both computationally and experimentally. The computational results indicate that N-SCAN is more accurate than any of the other tools we tested at predicting the TSS and the complete first exon. It is the only one of these tools that can predict complete gene structures together with 5' UTRs. Experimental evaluation shows that N-SCAN can be used to validate novel UTR introns in human gene predictions that do not overlap any RefSeq gene and even to correct RefSeq mRNAs by adding validated UTR exons that are missing from RefSeq.

PMID:15867435 | PMC:PMC1088303 | DOI:10.1101/gr.3696205

Genome Res. 2005 Apr;15(4):577-82. doi: 10.1101/gr.3329005.

ABSTRACT

The genome of Caenorhabditis elegans was the first animal genome to be sequenced. Although considerable effort has been devoted to annotating it, the standard WormBase annotation contains thousands of predicted genes for which there is no cDNA or EST evidence. We hypothesized that a more complete experimental annotation could be obtained by creating a more accurate gene-prediction program and then amplifying and sequencing predicted genes. Our approach was to adapt the TWINSCAN gene prediction system to C. elegans and C. briggsae and to improve its splice site and intron-length models. The resulting system has 60% sensitivity and 58% specificity in exact prediction of open reading frames (ORFs), and hence, proteins-the best results we are aware of any multicellular organism. We then attempted to amplify, clone, and sequence 265 TWINSCAN-predicted ORFs that did not overlap WormBase gene annotations. The success rate was 55%, adding 146 genes that were completely absent from WormBase to the ORF clone collection (ORFeome). The same procedure had a 7% success rate on 90 Worm Base "predicted" genes that do not overlap TWINSCAN predictions. These results indicate that the accuracy of WormBase could be significantly increased by replacing its partially curated predicted genes with TWINSCAN predictions. The technology described in this study will continue to drive the C. elegans ORFeome toward completion and contribute to the annotation of the three Caenorhabditis species currently being sequenced. The results also suggest that this technology can significantly improve our knowledge of the "parts list" for even the best-studied model organisms.

PMID:15805498 | PMC:PMC1074372 | DOI:10.1101/gr.3329005

Science. 2005 Feb 25;307(5713):1321-4. doi: 10.1126/science.1103773. Epub 2005 Jan 13.

ABSTRACT

Cryptococcus neoformans is a basidiomycetous yeast ubiquitous in the environment, a model for fungal pathogenesis, and an opportunistic human pathogen of global importance. We have sequenced its approximately 20-megabase genome, which contains approximately 6500 intron-rich gene structures and encodes a transcriptome abundant in alternatively spliced and antisense messages. The genome is rich in transposons, many of which cluster at candidate centromeric regions. The presence of these transposons may drive karyotype instability and phenotypic variation. C. neoformans encodes unique genes that may contribute to its unusual virulence properties, and comparison of two phenotypically distinct strains reveals variation in gene content in addition to sequence polymorphisms between the genomes.

PMID:15653466 | PMC:PMC3520129 | DOI:10.1126/science.1103773

Genome Res. 2004 Nov;14(11):2330-5. doi: 10.1101/gr.2816704. Epub 2004 Oct 12.

ABSTRACT

The genomes of clusters of related eukaryotes are now being sequenced at an increasing rate, creating a need for accurate, low-cost annotation of exon-intron structures. In this paper, we demonstrate that reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on predicted gene structures satisfy this need, at least for single-celled eukaryotes. The TWINSCAN gene prediction algorithm was adapted for the fungal pathogen Cryptococcus neoformans by using a precise model of intron lengths in combination with ungapped alignments between the genome sequences of the two closely related Cryptococcus varieties. This approach resulted in approximately 60% of known genes being predicted exactly right at every coding base and splice site. When previously unannotated TWINSCAN predictions were tested by RT-PCR and direct sequencing, 75% of targets spanning two predicted introns were amplified and produced high-quality sequence. When targets spanning the complete predicted open reading frame were tested, 72% of them amplified and produced high-quality sequence. We conclude that sequencing a small number of expressed sequence tags (ESTs) to provide training data, running TWINSCAN on an entire genome, and then performing RT-PCR and direct sequencing on all of its predictions would be a cost-effective method for obtaining an experimentally verified genome annotation.

PMID:15479946 | PMC:PMC525692 | DOI:10.1101/gr.2816704

Cold Spring Harb Symp Quant Biol. 2003;68:125-30. doi: 10.1101/sqb.2003.68.125.

NO ABSTRACT

PMID:15338610 | DOI:10.1101/sqb.2003.68.125

Dev Psychol. 2004 Jul;40(4):621-32. doi: 10.1037/0012-1649.40.4.621.

ABSTRACT

The authors asked whether there is evidence to support the existence of the vocabulary spurt, an increase in the rate of word learning that is thought to occur during the 2nd year of life. Using longitudinal data from 38 children, they modeled the rate of word learning with two functions, one with an inflection point (logistic), which would indicate a spurt, and one without an inflection point (quadratic). Comparing the fits of these two functions using likelihood ratios, they found that just 5 children had a better logistic fit, which indicated that these children underwent a spurt. The implications for theories of cognitive and language development are considered.

PMID:15238048 | DOI:10.1037/0012-1649.40.4.621

Curr Opin Struct Biol. 2004 Jun;14(3):264-72. doi: 10.1016/j.sbi.2004.05.007.

ABSTRACT

De novo gene predictors are programs that predict the exon-intron structures of genes using the sequences of one or more genomes as their only input. In the past two years, dual-genome de novo predictors, which exploit local rates and patterns of mutation inferred from alignments between two genomes, have led to significant improvements in accuracy. Systems that exploit more than two genomes simultaneously have only recently begun to appear and are not yet competitive on practical tasks, but offer the greatest hope for near-term improvements. Dual-genome de novo prediction for compact eukaryotic genomes such as those of Arabidopsis thaliana and Caenorhabditis elegans is already quite accurate. Although mammalian gene prediction lags behind in accuracy, it is yielding ever more useful results. Coupled with significant improvements in pseudogene detection methods, which have eliminated many false positives, we have reached the point where de novo gene predictions are being used as hypotheses to drive experimental annotation via systematic RT-PCR and sequencing.

PMID:15193305 | DOI:10.1016/j.sbi.2004.05.007

Genome Res. 2004 Apr;14(4):665-71. doi: 10.1101/gr.1959604.

ABSTRACT

The publication of a draft sequence of a third mammalian genome--that of the rat--suggests a need to rethink genome annotation. New mammalian sequences will not receive the kind of labor-intensive annotation efforts that are currently being devoted to human. In this paper, we demonstrate an alternative approach: reverse transcription-polymerase chain reaction (RT-PCR) and direct sequencing based on dual-genome de novo predictions from TWINSCAN. We tested 444 TWINSCAN-predicted rat genes that showed significant homology to known human genes implicated in disease but that were partially or completely missed by methods based on protein-to-genome mapping. Using primers in exons flanking a single predicted intron, we were able to verify the existence of 59% of these predicted genes. We then attempted to amplify the complete predicted open reading frames of 136 genes that were verified in the single-intron experiment. Spliced sequences were amplified in 46 cases (34%). We conclude that this procedure for elucidating gene structures with native cDNA sequences is cost-effective and will become even more so as it is further optimized.

PMID:15060008 | PMC:PMC383311 | DOI:10.1101/gr.1959604

PLoS Biol. 2003 Nov;1(2):E45. doi: 10.1371/journal.pbio.0000045. Epub 2003 Nov 17.

ABSTRACT

The soil nematodes Caenorhabditis briggsae and Caenorhabditis elegans diverged from a common ancestor roughly 100 million years ago and yet are almost indistinguishable by eye. They have the same chromosome number and genome sizes, and they occupy the same ecological niche. To explore the basis for this striking conservation of structure and function, we have sequenced the C. briggsae genome to a high-quality draft stage and compared it to the finished C. elegans sequence. We predict approximately 19,500 protein-coding genes in the C. briggsae genome, roughly the same as in C. elegans. Of these, 12,200 have clear C. elegans orthologs, a further 6,500 have one or more clearly detectable C. elegans homologs, and approximately 800 C. briggsae genes have no detectable matches in C. elegans. Almost all of the noncoding RNAs (ncRNAs) known are shared between the two species. The two genomes exhibit extensive colinearity, and the rate of divergence appears to be higher in the chromosomal arms than in the centers. Operons, a distinctive feature of C. elegans, are highly conserved in C. briggsae, with the arrangement of genes being preserved in 96% of cases. The difference in size between the C. briggsae (estimated at approximately 104 Mbp) and C. elegans (100.3 Mbp) genomes is almost entirely due to repetitive sequence, which accounts for 22.4% of the C. briggsae genome in contrast to 16.5% of the C. elegans genome. Few, if any, repeat families are shared, suggesting that most were acquired after the two species diverged or are undergoing rapid evolution. Coclustering the C. elegans and C. briggsae proteins reveals 2,169 protein families of two or more members. Most of these are shared between the two species, but some appear to be expanding or contracting, and there seem to be as many as several hundred novel C. briggsae gene families. The C. briggsae draft sequence will greatly improve the annotation of the C. elegans genome. Based on similarity to C. briggsae, we found strong evidence for 1,300 new C. elegans genes. In addition, comparisons of the two genomes will help to understand the evolutionary forces that mold nematode genomes.

PMID:14624247 | PMC:PMC261899 | DOI:10.1371/journal.pbio.0000045

BMC Bioinformatics. 2003 Oct 17;4:50. doi: 10.1186/1471-2105-4-50.

ABSTRACT

SUMMARY: Eval is a flexible tool for analyzing the performance of gene annotation systems. It provides summaries and graphical distributions for many descriptive statistics about any set of annotations, regardless of their source. It also compares sets of predictions to standard annotations and to one another. Input is in the standard Gene Transfer Format (GTF). Eval can be run interactively or via the command line, in which case output options include easily parsable tab-delimited files.

AVAILABILITY: To obtain the module package with documentation, go to http://genes.cse.wustl.edu/ and follow links for Resources, then Software. Please contact brent@cse.wustl.edu

PMID:14565849 | PMC:PMC270064 | DOI:10.1186/1471-2105-4-50