Biology subjects

Behera, S.

Publications and source records attributed to Behera, S..

25 records · Page 2Linked to original sources

bioRxiv2022

A pan-genome approach to decipher variants in the highly complex tandem repeat of LPA

Lp(a) is an important factor in Coronary Heart disease risk, and its expression is correlated with the length of the LPA gene. The KIV-2 region of LPA consists of variable tandem repeats whose number is highly variable across individuals. Little is known about the inner diversity of the KIV-2 repeats themselves. Here we utilize a pan-genome graph approach across 47 haplotype resolved assemblies to identify unexpected variation across KIV-2.

bioinformatics↗

bioRxiv2022

Comprehensive Structural Variant Detection: From Mosaic to Population-Level

Long-read Structural Variation (SV) calling remains a challenging but highly accurate way to identify complex genomic alterations. Here, we present Sniffles2, which is faster and more accurate than state-of-the-art SV caller across different coverages, sequencing technologies, and SV types. Furthermore, Sniffles2 solves the problem of family- to population-level SV calling to produce fully genotyped VCF files by introducing a gVCF file concept. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we successfully identified multiple mosaic SVs across a multiple system atrophy patient brain. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements. In summary, we demonstrate the utility and versatility of Sniffles2 to identify SVs from the mosaic to population levels.

bioinformatics↗

bioRxiv2020

A consensus-based ensemble approach to improve de novo transcriptome assembly

BackgroundSystems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. ResultsIn this study, we first provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. ConclusionsWithout using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

bioinformatics↗

bioRxiv2020

The G-protein coupled receptor SRX-97 is required for concentration dependent sensing of Benzaldehyde in Caenorhabditis elegans

The G-protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors in the olfactory system function to sense the surroundings and respond to various odorants. The genes encoding for the olfactory receptors in C. elegans are larger in number in comparison to those in mammals, suggesting complexity in the receptor- odorant relationships. Recent studies have shown that the same odorant in different concentration could act on multiple receptors in different neurons to induce attractive or repulsive responses. The ASH neuron is known to be responsible for responding to high concentrations of volatile odorants. Here we characterize a new GPCR, SRX-97. We found that the srx-97 promoter shows expression specifically in the head ASH and tail PHB chemosensory neurons of C. elegans. Further, the SRX-97 protein localizes to the ciliary ends of the ASH neurons. Analysis of CRISPR/based deletion mutants of the srx-97 gene suggest that this gene is involved in the recognition of high concentrations of benzaldehyde. This was further confirmed through rescue and neuronal ablation experiments. Our work gives insight into concentration dependent receptor function in the olfactory system and provides details of an additional molecule that could help the animal navigate its surroundings.

neuroscience↗

bioRxiv2019

Climatic Factors in Relation to Diarrhea for Informed Public Health Decision-Making: A Novel Methodological Approach

BackgroundDiarrheal disease is one of the leading causes of morbidity and mortality globally, particularly in children under 5 years of age. Factors related to diarrheal disease incidence include infection, malnutrition, and exposure to contaminated water and food. Climate factors also contribute to diarrheal disease. ObjectivesWe aimed to explore the relationship between temperature, precipitation and diarrhea case counts of hospital admissions among vulnerable communities living in a rural setting in South Africa. MethodsWe applied a novel approach of contour analysis to visually examine simultaneous observations in frequencies of anomalously high and low diarrhea case counts occurring in a season and assigning colors to differences that were statistically significant based on chi-squared test results. ResultsThere was a significantly positive difference between high and low groups when there was a lack of rain (0 mm of cumulative rain) for 1 to 2 weeks in winter for children under 5. Diarrhea prevalence was greater among children under 5 years when conditions were hotter than usual during winter and spring. DiscussionDry conditions may lead to increased water storage raising the risks of water contamination. Reduced use of water for personal hygiene and cleaning of outdoor pit latrines affect sanitation quality. Rural communities require adequate and uninterrupted water provision and healthcare providers should raise awareness about potential diarrheal risks especially during the dry season.

epidemiology↗

bioRxiv2018

The Ca2+ sensor protein CMI1 fine tunes root development, auxin distribution and responses

Signaling cross-talks between auxin, a regulator of plant development and Ca2+, a universal second messenger have been proposed to modulate developmental plasticity in plants. However, the underlying molecular mechanisms are largely unknown. Here we report that in Arabidopsis roots, auxin elicits specific Ca2+ signaling pattern that spatially coincide with the expression pattern of auxin-regulated genes. We identified the EF-hand protein CMI1 (Ca2+ sensor Modulator of ICR1) as an interactor of the ROP effector ICR1 (Interactor of Constitutively active ROP). CMI1 is monomeric in solution, changes its secondary structure at Ca2+ concentrations ranging from 10-9 to 10-8 M and its interaction with ICR1 is Ca2+ dependent, involving a conserved hydrophobic pocket. cmi1 mutants display an increased auxin response including shorter primary roots, longer root hairs, longer hypocotyls and altered lateral root formation while ectopic expression of CMI1 induces root growth arrest and reduced auxin responses at the root tip. When expressed alone, CMI1 is localized at the plasma membrane, the cytoplasm and in nuclei. Interaction of CMI1 and ICR1 results in exclusion of CMI1 from nuclei and suppression of the root growth arrest. CMI1 expression is directly upregulated by auxin while expression of auxin induced genes is enhanced in cmi1 concomitantly with repression of auxin induced Ca2+ increases in the lateral root cap and vasculature, indicating that CMI1 represses early auxin responses. Collectively, our findings identify a crucial function of Ca2+ signaling and CMI1 in root growth and suggest an auxin-Ca2+ regulatory feedback loop that fine tunes root development.

plant biology↗

bioRxiv2017

STAG-CNS: An Order-Aware Conserved Non-coding Sequences Discovery Tool For Arbitrary Numbers of Species

One method for identifying noncoding regulatory regions of a genome is to quantify rates of divergence between related species, as functional sequence will generally diverge more slowly. Most approaches to identifying these conserved noncoding sequences (CNS) based on alignment have had relatively large minimum sequence lengths ([>=]15 base pair) compared to the average length of known transcription factor binding sites. To circumvent this constraint, STAG-CNS integrates data from the promoters of conserved orthologous genes in three or more species simultaneously. Using data from up to six grass species made it possible to identify conserved sequences as short at 9 base pairs with FDP [<=] 0.05. These CNS exhibit greater overlap with open chromatin regions identified using DNase I hypersensitivity, and are enriched in the promoters of genes involved in transcriptional regulation. STAG-CNS was further employed to characterize loss of conserved noncoding sequences associated with retained duplicate genes from the ancient maize polyploidy. Genes with fewer retained CNS show lower overall expression, although this bias is more apparent in samples of complex organ systems containing many cell types, suggesting CNS loss may correspond to a reduced number of expression contexts rather than lower expression levels across the entire ancestral expression domain.

bioinformatics↗