首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 31 毫秒
1.
2.
Comparative gene prediction in human and mouse   总被引:14,自引:2,他引:14       下载免费PDF全文
The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.  相似文献   

3.
Recent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for ~100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only ~10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.  相似文献   

4.
Computational gene prediction using multiple sources of evidence   总被引:10,自引:1,他引:9       下载免费PDF全文
This article describes a computational method to construct gene models by using evidence generated from a diverse set of sources, including those typical of a genome annotation pipeline. The program, called Combiner, takes as input a genomic sequence and the locations of gene predictions from ab initio gene finders, protein sequence alignments, expressed sequence tag and cDNA alignments, splice site predictions, and other evidence. Three different algorithms for combining evidence in the Combiner were implemented and tested on 1783 confirmed genes in Arabidopsis thaliana. Our results show that combining gene prediction evidence consistently outperforms even the best individual gene finder and, in some cases, can produce dramatic improvements in sensitivity and specificity.  相似文献   

5.
Issac B  Raghava GP 《Genome research》2004,14(9):1756-1766
EGPred is a Web-based server that combines ab initio methods and similarity searches to predict genes, particularly exon regions, with high accuracy. The EGPred program proceeds in the following steps: (1) an initial BLASTX search of genomic sequence against the RefSeq database is used to identify protein hits with an E-value <1; (2) a second BLASTX search of genomic sequence against the hits from the previous run with relaxed parameters (E-values <10) helps to retrieve all probable coding exon regions; (3) a BLASTN search of genomic sequence against the intron database is then used to detect probable intron regions; (4) the probable intron and exon regions are compared to filter/remove wrong exons; (5) the NNSPLICE program is then used to reassign splicing signal site positions in the remaining probable coding exons; and (6) finally ab initio predictions are combined with exons derived from the fifth step based on the relative strength of start/stop and splice signal sites as obtained from ab initio and similarity search. The combination method increases the exon level performance of five different ab initio programs by 4%-10% when evaluated on the HMR195 data set. Similar improvement is observed when ab initio programs are evaluated on the Burset/Guigo data set. Finally, EGPred is demonstrated on an approximately 95-Mbp fragment of human chromosome 13. The list of predicted genes from this analysis are available in the supplementary material. The EGPred program is computationally intensive due to multiple BLAST runs during each analysis. The EGPred server is available at http://www.imtech.res.in/raghava/egpred/.  相似文献   

6.
7.
8.
The sequence of any genome becomes most useful for biological experimentation when a complete and accurate gene set is available. Gene prediction programs offer an efficient way to generate an automated gene set. Manual annotation, when performed by experienced annotators, is more accurate and complete than automated annotation. However, it is a laborious and expensive process, and by its nature, introduces a degree of variability not found with automated annotation. EAnnot (Electronic Annotation) is a program originally developed for manually annotating the human genome. It combines the latest bioinformatics tools to extract and analyze a wide range of publicly available data in order to achieve fast and reliable automatic gene prediction and annotation. EAnnot builds gene models based on mRNA, EST, and protein alignments to genomic sequence, attaches supporting evidence to the corresponding genes, identifies pseudogenes, and locates poly(A) sites and signals. Here, we compare manual annotation of human chromosome 6 with annotation performed by EAnnot in order to assess the latter's accuracy. EAnnot can readily be applied to manual annotation of other eukaryotic genomes and can be used to rapidly obtain an automated gene set.  相似文献   

9.
Genome annotation assessment in Drosophila melanogaster   总被引:4,自引:0,他引:4       下载免费PDF全文
Computational methods for automated genome annotation are critical to our community's ability to make full use of the large volume of genomic sequence being generated and released. To explore the accuracy of these automated feature prediction tools in the genomes of higher organisms, we evaluated their performance on a large, well-characterized sequence contig from the Adh region of Drosophila melanogaster. This experiment, known as the Genome Annotation Assessment Project (GASP), was launched in May 1999. Twelve groups, applying state-of-the-art tools, contributed predictions for features including gene structure, protein homologies, promoter sites, and repeat elements. We evaluated these predictions using two standards, one based on previously unreleased high-quality full-length cDNA sequences and a second based on the set of annotations generated as part of an in-depth study of the region by a group of Drosophila experts. Although these standard sets only approximate the unknown distribution of features in this region, we believe that when taken in context the results of an evaluation based on them are meaningful. The results were presented as a tutorial at the conference on Intelligent Systems in Molecular Biology (ISMB-99) in August 1999. Over 95% of the coding nucleotides in the region were correctly identified by the majority of the gene finders, and the correct intron/exon structures were predicted for >40% of the genes. Homology-based annotation techniques recognized and associated functions with almost half of the genes in the region; the remainder were only identified by the ab initio techniques. This experiment also presents the first assessment of promoter prediction techniques for a significant number of genes in a large contiguous region. We discovered that the promoter predictors' high false-positive rates make their predictions difficult to use. Integrating gene finding and cDNA/EST alignments with promoter predictions decreases the number of false-positive classifications but discovers less than one-third of the promoters in the region. We believe that by establishing standards for evaluating genomic annotations and by assessing the performance of existing automated genome annotation tools, this experiment establishes a baseline that contributes to the value of ongoing large-scale annotation projects and should guide further research in genome informatics.  相似文献   

10.
Improving gene annotation using peptide mass spectrometry   总被引:3,自引:1,他引:2       下载免费PDF全文
Annotation of protein-coding genes is a key goal of genome sequencing projects. In spite of tremendous recent advances in computational gene finding, comprehensive annotation remains a challenge. Peptide mass spectrometry is a powerful tool for researching the dynamic proteome and suggests an attractive approach to discover and validate protein-coding genes. We present algorithms to construct and efficiently search spectra against a genomic database, with no prior knowledge of encoded proteins. By searching a corpus of 18.5 million tandem mass spectra (MS/MS) from human proteomic samples, we validate 39,000 exons and 11,000 introns at the level of translation. We present translation-level evidence for novel or extended exons in 16 genes, confirm translation of 224 hypothetical proteins, and discover or confirm over 40 alternative splicing events. Polymorphisms are efficiently encoded in our database, allowing us to observe variant alleles for 308 coding SNPs. Finally, we demonstrate the use of mass spectrometry to improve automated gene prediction, adding 800 correct exons to our predictions using a simple rescoring strategy. Our results demonstrate that proteomic profiling should play a role in any genome sequencing project.  相似文献   

11.
12.
13.
14.
This study sets out to identify novel susceptibility genes for late-onset Alzheimer's disease (LOAD) in a powerful set of samples from the UK and USA (1808 LOAD cases and 2062 controls). Allele frequencies of 17 343 gene-based putative functional single nucleotide polymorphisms (SNPs) were tested for association with LOAD in a discovery case-control sample from the UK. A tiered strategy was used to follow-up significant variants from the discovery sample in four independent sample sets. Here, we report the identification of several candidate SNPs that show significant association with LOAD. Three of the identified markers are located on chromosome 19 (meta-analysis: full sample P = 6.94E - 81 to 0.0001), close to the APOE gene and exhibit linkage disequilibrium (LD) with the APOEepsilon4 and epsilon2/3 variants (0.09 < D'<1). Two of the three SNPs can be regarded as study-wide significant (expected number of false positives reaching the observed significance level less than 0.05 per study). Sixteen additional SNPs show evidence for association with LOAD [P = 0.0010-0.00006; odds ratio (OR) = 1.07-1.45], several of which map to known linkage regions, biological candidate genes and novel genes. Four SNPs not in LD with APOE show a false positive rate of less than 2 per study, one of which shows study-wide suggestive evidence taking account of 17 343 tests. This is a missense mutation in the galanin-like peptide precursor gene (P = 0.00005, OR = 1.2, false positive rate = 0.87).  相似文献   

15.
Conrad: gene prediction using conditional random fields   总被引:1,自引:0,他引:1       下载免费PDF全文
We present Conrad, the first comparative gene predictor based on semi-Markov conditional random fields (SMCRFs). Unlike the best standalone gene predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum likelihood, Conrad is discriminatively trained to maximize annotation accuracy. In addition, unlike the best annotation pipelines, which rely on heuristic and ad hoc decision rules to combine standalone gene predictors with additional information such as ESTs and protein homology, Conrad encodes all sources of information as features and treats all features equally in the training and inference algorithms. Conrad outperforms the best standalone gene predictors in cross-validation and whole chromosome testing on two fungi with vastly different gene structures. The performance improvement arises from the SMCRF's discriminative training methods and their ability to easily incorporate diverse types of information by encoding them as feature functions. On Cryptococcus neoformans, configuring Conrad to reproduce the predictions of a two-species phylo-GHMM closely matches the performance of Twinscan. Enabling discriminative training increases performance, and adding new feature functions further increases performance, achieving a level of accuracy that is unprecedented for this organism. Similar results are obtained on Aspergillus nidulans comparing Conrad versus Fgenesh. SMCRFs are a promising framework for gene prediction because of their highly modular nature, simplifying the process of designing and testing potential indicators of gene structure. Conrad's implementation of SMCRFs advances the state of the art in gene prediction in fungi and provides a robust platform for both current application and future research.  相似文献   

16.
An assessment of gene prediction accuracy in large DNA sequences   总被引:11,自引:2,他引:11       下载免费PDF全文
One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.  相似文献   

17.
18.
19.
《Genetics in medicine》2017,19(5):496-504
PurposeClassification of novel variants is a major challenge facing the widespread adoption of comprehensive clinical genomic sequencing and the field of personalized medicine in general. This is largely because most novel variants do not have functional, genetic, or population data to support their clinical classification.MethodsTo improve variant interpretation, we leveraged the Exome Aggregation Consortium (ExAC) data set (N = ~60,000) as well as 7,000 clinically curated variants in 132 genes identified in more than 11,000 probands clinically tested for cardiomyopathies, rasopathies, hearing loss, or connective tissue disorders to perform a systematic evaluation of domain level disease associations.ResultsWe statistically identify regions that are most sensitive to functional variation in the general population and also most commonly impacted in symptomatic individuals. Our data show that a significant number of exons and domains in genes strongly associated with disease can be defined as disease-sensitive or disease-tolerant, leading to potential reclassification of at least 26% (450 out of 1,742) of variants of uncertain clinical significance in the 132 genes.ConclusionThis approach leverages domain functional annotation and associated disease in each gene to prioritize candidate disease variants, increasing the sensitivity and specificity of novel variant assessment within these genes.Genet Med advance online publication 22 September 2016  相似文献   

20.
GFScan: a gene family search tool at genomic DNA level   总被引:1,自引:0,他引:1  
Xuan Z  McCombie WR  Zhang MQ 《Genome research》2002,12(7):1142-1149
We have developed GFScan(Gene Family Scan), a tool that identifies members of a gene family by searching genomic DNA sequences with genomic DNA motifs (or matrices) that are representative of the family. We have tested GFScan on four human gene families including the neurotransmitter-gated ion-channels (NGIC) family, the carbonic anhydrases (CA) family, the Dbl homology (DH) domain family, and the ETS-domain family. All known members of these families with motifs mapped to sequenced genomic DNA regions were found, whereas some novel genomic locations were also found to match the motifs, which may indicate new members in these families. Compared with other methods, GFScan recognized all true positives with much fewer false positives. We also showed that motifs constructed based on human genes could be used to search the mouse genome to identify orthologous family members in mouse. This program is available at http://www.cshl.org/mzhanglab/.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号