89 research outputs found
GFF Utilities: GffRead and GffCompare
No full textclassify, merge, tracking and annotation of GFF files by comparing to a reference annotation GFFPlease cite as:
Pertea G and Pertea M. "GFF Utilities: GffRead and GffCompare". F1000Research 2020, 9:304 DOI: 10.12688/f1000research.23297.
GffCompare
No full textclassify, merge, tracking and annotation of GFF files by comparing to a reference annotation GFFPlease cite as: Pertea G and Pertea M. "GFF Utilities: GffRead and GffCompare". F1000Research 2020, 9:304 DOI: 10.12688/f1000research.23297.
TieBrush: an efficient method for aggregating and summarizing mapped reads across large datasets
No full textSUMMARY: Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. AVAILABILITY AND IMPLEMENTATION: TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
gpertea/gffread: v0.11.8
No full textGFF/GTF utility providing format conversions, region filtering, FASTA sequence extraction and mor
gpertea/gffcompare: v0.11.6
No full textclassify, merge, tracking and annotation of GFF files by comparing to a reference annotation GF
Detection of lineage-specific evolutionary changes among primate species
No full textAbstract Background Comparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection. Results We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection. Conclusions DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.</p
gpertea/stringtie:
No full textThis release primarily fixes an issue with coverage estimate calculation for long reads
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