Biology subjects

Babaian, A.

Publications and source records attributed to Babaian, A..

3 recordsLinked to original sources

bioRxiv2017

bioSyntax: Syntax Highlighting For Computational Biology

Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information that is obfuscated by the complexity of the data structures. bioSyntax (http://bioSyntax.org) is a freely available suite of syntax highlighting packages for vim, gedit, Sublime, and less, which aids computational scientists to parse and work with their data more efficiently.

bioinformatics↗

bioRxiv2017

LIONS: Analysis Suite for Detecting and Quantifying Transposable Element Initiated Transcription from RNA-seq

SummaryTransposable Elements (TEs) influence the evolution of novel transcriptional networks yet the specific and meaningful interpretation of how TE-initiation events contribute to the transcriptome has been marred by computational and methodological deficiencies. We developed LIONS for the analysis of paired-end RNA-seq data to specifically detect and quantify TE-initiated transcripts.\n\nAvailabilitySource code, container, test data and instruction manual are freely available at www.github.com/ababaian/LIONS.\n\nContactababaian@bccrc.ca or mahdi.karimi@lms.mrc.ac.uk or dmager@bccrc.ca.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Intra- and Inter-individual genetic variation in human ribosomal RNAs

The ribosome is an ancient RNA-protein complex essential for translating DNA to protein. At its core are the ribosomal RNAs (rRNAs), the most abundant RNA in the cell. To support high levels of transcription, repetitive arrays of ribosomal DNA (rDNA) are necessary and the long-standing hypothesis is that they undergo sequence homogenization towards rDNA uniformity.\n\nHere I present evidence of the rich genetic diversity in human rDNA, both within and between individuals. Using state-of-the-art genome sequencing data revealed an average of 192.7 intra-individual variants, including some deeply penetrating the rDNA copies, such as the bi-allelically expressed 28S.59A>G. From 104 diverse genomes, 947 high-confidence variants were identified and unmask a hidden genetic diversity of humans.\n\nThese findings support the emerging concept that ribosomes are heterogeneous within cells and extends the heterogeneity into the realm of population genetics. Fundamentally, do our ribosomal variants determine how our cells interpret the genome?

genomics↗