Computational inference of homologous gene structures in the human genome.

By Rufang Yeh, Lee P. Lim, C. Burge. Genome Research, 2001

418 citations recorded by the source corpus.

With the human genome sequence approaching completion, a major challenge is to identify the locations and encoded protein sequences of all human genes. To address this problem we have developed a new gene identification algorithm, GenomeScan, which combines exon-intron and splice signal models with similarity to known protein sequences in an integrated model. Extensive testing shows that GenomeScan can accurately identify the exon-intron structures of genes in finished or draft human genome sequence with a low rate of false-positives. Application of GenomeScan to 2.7 billion bases of human genomic DNA identified at least 20,000-25,000 human genes out of an estimated 30,000-40,000 present in the genome. The results show an accurate and efficient automated approach for identifying genes in higher eukaryotic genomes and provide a first-level annotation of the draft human genome.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e8627056668fecbab2cd825aa741b0bf7703880c

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Citations in the corpus

Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. (2003, methodology): “In addition, tools have been developed to incorporate EST alignments into gene predictions (14–16).”
Integrating genomic homology into gene structure prediction (2001, methodology): “This dichotomy of methods has broken down in recent years with the invention of hybrid approaches that integrate EST or protein similarities into ab initio methods (Hooper et al., 2000; Reese et al., 2000a; Yeh et al., 2001).”
Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources (2006, methodology): “In addition to some ab initio methods, we tested GenomeScan [14] which uses BLASTX alignments with protein sequences for the prediction and TWINSCAN [6], a reimplementation of GENSCAN that uses in this setting BLASTN alignments with the mouse genomic sequence.”
Current methods of gene prediction, their strengths and weaknesses. (2002, methodology): “GenomeScan (99) is Burge's own extension of Genscan to incorporate similarity with a protein retrieved by BLASTX or BLASTP.”
The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution (2013, neutral): “5 (91) ‘Peach’ x ‘Ferganensis’ (P x F) 21 1321 (48) 207.”
A graphical model for protein secondary structure prediction (2004, methodology): “Generalized HMMs with explicit state duration, also known as segmental semi-Markov models (SSMMs), have been widely applied in the ﬁeld of gene identiﬁcation (Burge & Karlin, 1997; Yel et al., 2001; Zhang et al., 2003; Korf et al., 2001).”
Principles of Gene Manipulation and Genomics (2006, methodology): “The latest generation of gene-prediction algorithms, such as Grail/Exp, GenieEST and GenomeScan (Yeh et al. 2001), combine ab initio predictions with similarity data into a single probability model.”
Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome. (2017, methodology): “Gene models were predicted by homology-based predictors, FGENESH+ (Salamov and Solovyev, 2000), FGENESH_EST (similar to FGENESH+, EST as splice site and intron input instead of protein/translated ORF), and GenomeScan (Yeh et al., 2001), and from AUGUSTUS via BRAKER1 (Hoff et al.”
Genome Annotation in Plants and Fungi: EuGene as a Model Platform (2008, methodology): “After some hybrid gene finding systems, such as GenomScan [13], TwinScan [3], or even the recent NSCAN [4], the general trend is toward highly integrative gene finders.”
Gene prediction with a hidden Markov model and a new intron submodel (2003, methodology): “Programs as GENEWISE (Birney and Durbin, 1997), PROCRUSTES (Gelfand et al., 1996) and GENOMES-CAN Yeh et al. (2001) make use of a homology to a known protein.”