PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions

By Michael F. Lin, Irwin Jungreis, Manolis Kellis. Bioinform., 2010

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Motivation: As high-throughput transcriptome sequencing provides evidence for novel transcripts in many species, there is a renewed need for accurate methods to classify small genomic regions as protein coding or non-coding. We present PhyloCSF, a novel comparative genomics method that analyzes a multispecies nucleotide sequence alignment to determine whether it is likely to represent a conserved protein-coding region, based on a formal statistical comparison of phylogenetic codon models. Results: We show that PhyloCSF's classification performance in 12-species Drosophila genome alignments exceeds all other methods we compared in a previous study. We anticipate that this method will be widely applicable as the transcriptomes of many additional species, tissues and subcellular compartments are sequenced, particularly in the context of ENCODE and modENCODE, and as interest grows in long non-coding RNAs, often initially recognized by their lack of protein coding potential rather than conserved RNA secondary structures. Availability and Implementation: The Objective Caml source code and executables for GNU/Linux and Mac OS X are freely available at http://compbio.mit.edu/PhyloCSF Contact: mlin@mit.edu; manoli@mit.edu

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/21693c2c5ed8a10a70d377de8661dc799c550bf0

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Citations in the corpus

GENCODE 2021 (2020, methodology): “We have completed the first pass manual annotation of the mouse reference genome based on experiences on completing the human annotation in 2013 and have used whole genome PhyloCSF (1) analysis to generate ranked lists of candidate coding regions for investigation by expert human annotators.”
Ribosome profiling provides evidence that large non-coding RNAs do not encode proteins (2013, methodology): “In contrast, methods such as CSF demonstrate sharper discriminative power between coding and noncoding transcripts (Guttman et al., 2009, 2010) and are therefore more reliable on a per-transcript basis, but they fail to detect proteins that are not conserved (Lin et al., 2011).”
Transcript expression-aware annotation improves rare variant discovery and interpretation (2019, methodology): “These variants often affected poorly conserved exons, as determined by PhyloP( 38 ), PhyloCSF( 19 ), and visualization in the UCSC browser( 37 ).”
Pervasive Transcription of the Human Genome Produces Thousands of Previously Unidentified Long Intergenic Noncoding RNAs (2013, methodology): “The program PhyloCSF (9/16/2010 release) [40] was used to computationally evaluate the coding potential of the filtered lincRNA transcripts.”
Variant interpretation using population databases: lessons from gnomAD (2021, background): “…pLoF variants in gnomAD have additional flags (i.e., single exon genes, poor conservation by Phylogenetic Codon Substitution Frequencies (PhyloCSF) (Lin et al., 2011), NAGNAG splice acceptor sites, and noncanonical splice sites) that can be found on the variant page together with the VEP…”
Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis. (2012, methodology): “Analysis of coding potential by PhyloCSF PhyloCSF (Lin et al. 2011) was used to assess coding potential in the transcripts based on evolutionary signatures in the five-fish genome alignment.”