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Abstract We used long-read DNA sequencing to assemble the genome of a Southern Han Chinese male. We organized the sequence into chromosomes and filled in gaps using the recently completed T2T-CHM13 genome as a guide, yielding a gap-free genome, Han1, containing 3,099,707,698 bases. Using the T2T-CHM13 annotation as a reference, we mapped all genes onto the Han1 genome and identified additional gene copies, generating a total of 60,708 putative genes, of which 20,003 are protein-coding. A comprehensive comparison between the genes revealed that 235 protein-coding genes were substantially different between the individuals, with frameshifts or truncations affecting the protein-coding sequence. Most of these were heterozygous variants in which one gene copy was unaffected. This represents the first gene-level comparison between two finished, annotated individual human genomes. 

Abstract Summary 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. 

Summary: GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations. Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license  ( https://github.com/gpertea/gffread , https://github.com/gpertea/gffcompare ).