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Seetharam, A.

Publications and source records attributed to Seetharam, A..

6 recordsLinked to original sources

A pan-tissue, pan-disease compendium of human orphan genes

Species-specific genes are ubiquitous in evolution, with functions ranging from prey paralysis to survival in subzero temperatures. Because they are typically expressed under limited conditions and lack canonical features, such genes may be vastly under-identified, even in humans. Here, we leverage terabytes of human RNA-Seq data to identify thousands of highly-expressed transcripts that do not correspond to any Gencode-annotated gene. Many may be novel ncRNAs although 80% of them contain ORFs that have the potential of encoding proteins unique to Homo sapiens (orphan genes). We validate our findings with independent strand-specific and single-cell RNA-seq datasets. Hundreds of these novel transcripts overlap with deleterious genomic variants; thousands show significant association with disease-specific patient survival. Most are dynamically regulated and accumulate selectively in particular tissues, cell-types, developmental stages, tumors, COVID-19, sex, and ancestries. As such, these transcripts hold potential as diagnostic biomarkers or therapeutic targets. To empower future discovery, we provide a compendium of these huge RNA-Seq expression data, and RiboSeq data, with associated metadata. Further, we supply the gene models for the novel genes as UCSC Genome Browser tracks.

evolutionary biology↗

MaizeCODE reveals bi-directionally expressed enhancers that harbor molecular signatures of maize domestication.

Modern maize was domesticated from Teosinte parviglumis, with subsequent introgressions from Teosinte mexicana, yielding increased kernel row number, loss of the hard fruit case and dissociation from the cob upon maturity, as well as fewer tillers. Molecular approaches have identified several transcription factors involved in the development of these traits, yet revealed that a complex regulatory network is at play. MaizeCODE deploys ENCODE strategies to catalog regulatory regions in the maize genome, generating histone modification and transcription factor ChIP-seq in parallel with transcriptomics datasets in 5 tissues of 3 inbred lines which span the phenotypic diversity of maize, as well as the teosinte inbred TIL11. Integrated analysis of these datasets resulted in the identification of a comprehensive set of regulatory regions in each inbred, and notably of distal enhancers which were differentiated from gene bodies by their lack of H3K4me1. Many of these distal enhancers expressed non- coding enhancer RNAs bi-directionally, reminiscent of "super enhancers" in animal genomes. We show that pollen grains are the most differentiated tissue at the transcriptomic level, and share features with endosperm that may be related to McClintocks chromosome breakage- fusion-bridge cycle. Conversely, ears have the least conservation between maize and teosinte, both in gene expression and within regulatory regions, reflecting conspicuous morphological differences selected during domestication. The identification of molecular signatures of domestication in transcriptional regulatory regions provides a framework for directed breeding strategies in maize.

plant biology↗

Effect of Sequence Depth and Length in Long-read Assembly of the Maize Inbred NC358

Recent improvements in the quality and yield of long-read data and scaffolding technology have made it possible to rapidly generate reference-quality assemblies for complex genomes. Still, generating these assemblies is costly, and an assessment of critical sequence depth and read length to obtain high-quality assemblies is important for allocating limited resources. To this end, we have generated eight independent assemblies for the complex genome of the maize inbred line NC358 using PacBio datasets ranging from 20-75x genomic depth and N50 read lengths of 11-21 kb. Assemblies with 30x or less depth and N50 read length of 11 kb were highly fragmented, with even the low-copy genic fraction of the genome showing degradation at 20x depth. Distinct sequence-quality thresholds were observed for complete assembly of genes, transposable elements, and highly repetitive genomic features such as telomeres, heterochromatic knobs and centromeres. This study provides a useful resource allocation reference to the community as long-read technologies continue to mature.

genomics↗

GenomeQC: A quality assessment tool for genome assemblies and gene structure annotations

BackgroundGenome assemblies are foundational for understanding the biology of a species. They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, genomic diversity and differences in gene expression across individuals and tissue types. Quality metrics for genome assemblies gauge both the completeness and contiguity of an assembly and help provide confidence in downstream biological insights. To compare quality across multiple assemblies, a set of common metrics are typically calculated and then compared to one or more gold standard reference genomes. While several tools exist for calculating individual metrics, applications providing comprehensive evaluations of multiple assembly features are, perhaps surprisingly, lacking. Here, we describe a new toolkit that integrates multiple metrics to characterize both assembly and gene annotation quality in a way that enables comparison across multiple assemblies and assembly types.\n\nFindingsOur application, named GenomeQC, is an easy-to-use and interactive web framework that integrates various quantitative measures to characterize genome assemblies and annotations. GenomeQC provides researchers with a comprehensive summary of these statistics and allows for benchmarking against gold standard reference assemblies.\n\nConclusionsThe GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license.\n\nAll source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.

bioinformatics↗

Pervasive off-target and double-stranded DNA nicking by CRISPR-Cas12a

Cas12a (formerly Cpf1) is an RNA-guided endonuclease in the CRISPR-Cas immune system that can be easily programmed for genome editing. Cas12a can bind and cut dsDNA targets with high specificity in vivo, making it an ideal candidate for precise genome editing applications. This specificity is contradictory to the natural role of Cas12a as an immune effector against rapidly evolving phages. However, the native cleavage specificity and activity remains to be fully understood. We employed high-throughput in vitro cleavage assays to determine and compare the native specificities of three Cas12a orthologs. Surprisingly, we observed pervasive nicking of randomized target libraries, with strong nicking activity observed against targets with up to four mismatches. Nicking and cleavage activities are dependent on mismatch type and position, and vary depending on the Cas12a ortholog and crRNA sequence. Our high-throughput and biochemical analysis further reveal that Cas12a has robust activated non-specific nicking and weak non-specific dsDNA degradation activity in trans. Together, our findings reveal Cas12a cleavage activities that could be beneficial in the context of bacterial CRISPR-Cas immunity but may be detrimental for genome editing technology.

biochemistry↗

phylostratr: A framework for phylostratigraphy

MotivationThe goal of phylostratigraphy is to infer the evolutionary origin of each gene in an organism. Currently, there are no general pipelines for this task. We present an R package, phylostratr, to fill this gap, making high-quality phylostratigraphic analysis accessible to non-specialists.\n\nResultsPhylostratigraphic analysis entails searching for homologs within increasingly broad clades. The highest clade that contains all homologs of a gene is that genes phylostratum. We have created a general R-based framework, phylostratr, for estimating the phylostratum of every gene in a species. The program can fully automate an analysis: select species for a balanced representation of each strata, retrieve the sequences from UniProt, build BLAST databases, run BLAST, infer homologs for each gene against each subject species, determine phylostrata, and return summaries and diagnostics. phylostratr allows extensive customization. A user may: modify the automatically-generated clade tree or use their own tree; provide custom sequences in place of those automatically retrieved from UniProt; replace BLAST with an alternative algorithm; or tailor the method and sensitivity of the homology inference classifier. phylostratr also offers proteome quality assessments, false-positive diagnostics, and checks for missing organelle genomes. We show the utility of phylostratr through case studies in Arabidopsis thaliana and Saccharomyces cerevisiae.\n\nAvailabilityphylostratr source code and vignettes are available on GitHub at https://github.com/arendsee/phylostratr\n\nContactevewurtele@gmail.com

bioinformatics↗