Biology subjects

Johnson, L. K.

Publications and source records attributed to Johnson, L. K..

3 recordsLinked to original sources

bioRxiv2019

Tximeta: reference sequence checksums for provenance identification in RNA-seq

Correct annotation metadata is critical for reproducible and accurate RNA-seq analysis. When files are shared publicly or among collaborators with incorrect or missing annotation metadata, it becomes difficult or impossible to reproduce bioinformatic analyses from raw data. It also makes it more difficult to locate the transcriptomic features, such as transcripts or genes, in their proper genomic context, which is necessary for overlapping expression data with other datasets. We provide a solution in the form of an R/Bioconductor package tximeta that performs numerous annotation and metadata gathering tasks automatically on behalf of users during the import of transcript quantification files. The correct reference transcriptome is identified via a hashed checksum stored in the quantification output, and key transcript databases are downloaded and cached locally. The computational paradigm of automatically adding annotation metadata based on reference sequence checksums can greatly facilitate genomic workflows, by helping to reduce overhead during bioinformatic analyses, preventing costly bioinformatic mistakes, and promoting computational reproducibility. The tximeta package is available at https://bioconductor.org/packages/tximeta.

bioinformatics↗

bioRxiv2019

Draft genome assemblies using sequencing reads from Oxford Nanopore Technology and Illumina platforms for four species of North American killifish from the Fundulus genus

Draft de novo reference genome assemblies were obtained from four North American killifish species (Fundulus xenicus, Fundulus catenatus, Fundulus nottii, and Fundulus olivaceus) using sequence reads from Illumina and Oxford Nanopore Technologies PromethION platforms. For each species, the PromethION platform was used to generate 30-45x sequence coverage, and the Illumina platform was used to generate 50-160x sequence coverage. Contig N50 values ranged from 0.4 Mb to 2.7 Mb, and BUSCO scores were consistently above 90% complete using the Eukaryota database. Draft assemblies and raw sequencing data are available for public use. We encourage use and re-use of these data for assembly benchmarking and external analyses.

genomics↗

bioRxiv2018

Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes

BackgroundDe novo transcriptome assemblies are required prior to analyzing RNAseq data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or \"pipelines\", on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short read data collected by the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP). The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research (NCGR).\n\nResultsNew transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics, with assemblies from the Dinoflagellata and Ciliophora phyla showing a higher percentage of open reading frames and number of contigs than transcriptomes from other phyla.\n\nConclusionsGiven current bioinformatics approaches, there is no single best reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally-intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.\n\nKey PointsO_LIRe-assembly with new tools can yield new results\nC_LIO_LIAutomated and programmable pipelines can be used to process arbitrarily many samples.\nC_LIO_LIAnalyzing many samples using a common pipeline identifies taxon-specific trends.\nC_LI

bioinformatics↗