Biology subjects

Busby, B.

Publications and source records attributed to Busby, B..

3 recordsLinked to original sources

bioRxiv2018

GeneHummus: A pipeline to define gene families and their expression in legumes and beyond

During the last decade, plant biotechnological laboratories have sparked a monumental revolution with the rapid development of next sequencing technologies at affordable prices. Soon, these sequencing technologies and assembling of whole genomes will extend beyond the plant computational biologists and become commonplace within the plant biology disciplines. The current availability of large-scale genomic resources for non-traditional plant model systems (the so-called orphan crops) is enabling the construction of high-density integrated physical and genetic linkage maps with potential applications in plant breeding. The newly available fully sequenced plant genomes represent an incredible opportunity for comparative analyses that may reveal new aspects of genome biology and evolution. Analysis of the expansion and evolution of gene families across species is a common approach to infer biological functions. To date, the extent and role of gene families in plants has only been partially addressed and many gene families remain to be investigated. Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family, typically combining numerous BLAST searches and manually cleaning data. Due to the increasing abundance of genome sequences and the agronomical interest in plant gene families, the field needs a clear, automated annotation tool. Here, we present the GeneHummus pipeline, a step-by-step R-based pipeline for the identification, characterization and expression analysis of plant gene families. The impact of this pipeline comes from a reduction in hands-on annotation time combined with high specificity and sensitivity in extracting only proteins from the RefSeq database and providing the conserved domain architectures based on SPARCLE. As a case study we focused on the auxin receptor factors gene (ARF) family in Cicer arietinum (chickpea) and other legumes. We anticipate that our pipeline should be suitable for any plant gene family, and likely other gene families, vastly improving the speed and ease of genomic data processing.

bioinformatics↗

bioRxiv2018

Magic-BLAST, a accurate DNA and RNA-seq aligner for long and short reads

Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. It uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.

bioinformatics↗

bioRxiv2018

Comprehensive variant effect predictions of single nucleotide variants in model organisms

The effect of single nucleotide variants (SNVs) in coding and non-coding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this we compiled and benchmarked sequence and structure-based variant effect predictors and we analyzed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of H. sapiens, S. cerevisiae and E. coli. Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc, a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

genomics↗