A Computer Program for Aligning a cDNA Sequence with a Genomic DNA Sequence

Florea, Liliana; Hartzell, George W.; Zhang, Zheng; Rubin, Gerald M.; Miller, Webb

doi:10.1101/gr.8.9.967

Cited by 672 publications

(553 citation statements)

References 28 publications

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“…The gene transcript derived from the previous annotation was considered as another EST to the gene. The alignments of transcript and ESTs to genomic sequences were performed using the sim4 program, 31 which unveiled the boundaries of exons and introns. The large amount of information carried in ESTs was merged and integrated into a single splicing graph.…”

Section: Methodsmentioning

confidence: 99%

Detection of Alternative Splice Variants at the Proteome Level in Aspergillus flavus

et al. 2010

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Identification of proteins from proteolytic peptides or intact proteins plays an essential role in proteomics. Researchers use search engines to match the acquired peptide sequences to the target proteins. However, search engines depend on protein databases to provide candidates for consideration. Alternative splicing (AS), the mechanism where the exon of pre-mRNAs can be spliced and rearranged to generate distinct mRNA and therefore protein variants, enable higher eukaryotic organisms, with only a limited number of genes, to have the requisite complexity and diversity at the proteome level. Multiple alternative isoforms from one gene often share common segments of sequences. However, many protein databases only include a limited number of isoforms to keep minimal redundancy. As a result, the database search might not identify a target protein even with high quality tandem MS data and accurate intact precursor ion mass. We computationally predicted an exhaustive list of putative isoforms of Aspergillus flavus proteins from 20 371 expressed sequence tags to investigate whether an alternative splicing protein database can assign a greater proportion of mass spectrometry data. The newly constructed AS database provided 9807 new alternatively spliced variants in addition to 12 832 previously annotated proteins. The searches of the existing tandem MS spectra data set using the AS database identified 29 new proteins encoded by 26 genes. Nine fungal genes appeared to have multiple protein isoforms. In addition to the discovery of splice variants, AS database also showed potential to improve genome annotation. In summary, the introduction of an alternative splicing database helps identify more proteins and unveils more information about a proteome.

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Section: Methodsmentioning

confidence: 99%

Detection of Alternative Splice Variants at the Proteome Level in Aspergillus flavus

et al. 2010

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“…The human genome dataset we used is thus highly redundant but can easily be reduced to one of the available assemblies. For each pair of matching RNA and genomic sequence, local alignments were generated using sim4 (Florea et al 1998) with the parameters W = 15, R = 0, A = 4, and P = 1. The output of sim4 was filtered to eliminate all alignments that did not contain at least one region (exon) matching with at least 95% identity >30 nt.…”

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Long-Range Heterogeneity at the 3′ Ends of Human mRNAs

Iseli¹,

Stevenson²,

Souza³

et al. 2002

Genome Res.

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The publication of a draft of the human genome and of large collections of transcribed sequences has made it possible to study the complex relationship between the transcriptome and the genome. In the work presented here, we have focused on mapping mRNA 3Ј ends onto the genome by use of the raw data generated by the expressed sequence tag (EST) sequencing projects. We find that at least half of the human genes encode multiple transcripts whose polyadenylation is driven by multiple signals. The corresponding transcript 3Ј ends are spread over distances in the kilobase range. This finding has profound implications for our understanding of gene expression regulation and of the diversity of human transcripts, for the design of cDNA microarray probes, and for the interpretation of gene expression profiling experiments.

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“…Note that the concept of a match is not novel-it has been used implicitly by programs such as Sim4 (Florea et al 1998), est_genome (Mott 1997) and Spidey (Wheelan et al 2001) to align mRNA sequences to genomic sequences. In fact, even the construction of a gapped BLAST hit from ungapped hsps embodies this concept (although, of course, there are additional parameters like gap penalties at work in this case).…”

Section: Discussion Match As Unit Of Similaritymentioning

confidence: 99%

Visualizing Sequence Similarity of Protein Families

Veeramachaneni

Makałowski²

2004

Genome Res.

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Classification of proteins into families is one of the main goals of functional analysis. Proteins are usually assigned to a family on the basis of the presence of family-specific patterns, domains, or structural elements. Whereas proteins belonging to the same family are generally similar to each other, the extent of similarity varies widely across families. Some families are characterized by short, well-defined motifs, whereas others contain longer, less-specific motifs. We present a simple method for visualizing such differences. We applied our method to the Arabidopsis thaliana families listed at The Arabidopsis Information Resource (TAIR) Web site and for 76% of the nontrivial families (families with more than one member), our method identifies simple similarity measures that are necessary and sufficient to cluster members of the family together. Our visualization method can be used as part of an annotation pipeline to identify potentially incorrectly defined families. We also describe how our method can be extended to identify novel families and to assign unclassified proteins into known families.Genome projects (Bernal et al. 2001) are generating sequence data at a much faster rate than can be effectively analyzed. The goal of functional genomics is to determine the function of proteins predicted by these sequencing projects (Bork et al. 1998;Eisenberg et al. 2000;Tsoka and Ouzounis 2000). Because experimental evidence about individual proteins is difficult to obtain, a common strategy is to classify proteins into families on the basis of the presence of shared features or by clustering using some similarity measure. The underlying assumption is that members of the same family may possess similar or identical biochemical functions (Hegyi and Gerstein 1999) and that one can assign the functions of well-characterized members of a family to other members whose functions are not known or not well understood (Heger and Holm 2000).The simplest methods for clustering proteins into families rely on sequence-similarity measures, such as those obtained by BLAST (Altschul et al. 1990). More sophisticated approaches detect domains using domain databases (Bateman et al. 2002;Servant et al. 2002;Mulder et al. 2003), optionally use the order of domains as a fingerprint for the protein, and classify proteins into families on the basis of the presence of shared domains or similar domain architecture (Geer et al. 2002). Classification of proteins into families using structural similarities (Holm and Sander 1996) is, at present, limited by the relatively small number of structures available in PDB ( Similarity-based clustering is a two-step process-one first needs to determine pairwise similarities between all pairs of proteins and then apply a clustering method that uses the similarity matrix to group proteins into clusters. However, methods that quantify similarity by using some attribute of the best BLAST hit and use single-linkage clustering are not always successful. One problem such methods face is the detection of th...

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A Computer Program for Aligning a cDNA Sequence with a Genomic DNA Sequence

Cited by 672 publications

References 28 publications

Detection of Alternative Splice Variants at the Proteome Level in Aspergillus flavus

Detection of Alternative Splice Variants at the Proteome Level in Aspergillus flavus

Long-Range Heterogeneity at the 3′ Ends of Human mRNAs

Visualizing Sequence Similarity of Protein Families

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