Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2021.03.01.433223

GMSECT: Genome-Wide Massive Sequence Exhaustive Com-parison Tool for structural and copy number variations

Singh, A.
View original source ↗Export metadata ↓

Abstract

GMSECT is a parallel robust Application Interface that efficiently handles the large genomic sequences for rapid and efficient processing. It is a message passing interface based parallel computing Tool that can be operated on a cluster for Massive Sequences Exhaustive Comparison, to identify matches such as the structural variants. The GMSECT algorithm can be implemented using other parallel application programming interfaces as well such as Posix-Threads or can even be implemented in a serial submission fashion. There is complete flexibility to the choice of comparison tool that can be deployed and with the optional parameters as of the choice of comparison tools to suit the speed, sensitivity and specificity of pair wise alignment. The algorithm is simple and robust, and can be applied to compare multiple genomes, chromosomes or large sequences, of different individuals for personalized genome comparison and works good for homologous as well as distant species. The tool can even be applied to smaller genomes like the microbial genome such as the Escherichia coli or algae such as Chlamydomonas reihardtti or yeast Saccharomyces cerevisiae to quickly conduct comparisons, and thus finds its application to the pharmaceuticals and microbial product based firms for research and development. The application Interface can efficiently and rapidly compare massive sequences to detect for the presence of numerous types of DNA variation existing in the genome ranging from single nucleotide polymorphism (SNPs) to larger structural alterations, such as copy-number variants (CNVs) and inversions. The new algorithm has been tested for comparing the chromosome 21 of Celeras R27c compilation with all the 48 chromosomes of Celeras R27c compilation and with all the 48 chromosomes of the human Build 35 reference sequence, which took just 2 Hours and 10 minutes using the pair wise blast algorithm choice and with 110 processors each with 2.2 GHz capacity and 2 GB memory. GMSECT facilitates rapid scanning and interpretation in personalized sequencing project. The application interface with the above resources and alignment choice is estimated to do exhaustive comparison of the human genome with itself in just 2.35 days. An exhaustive comparison of an individuals genome with a reference genome would comprise of a two self genome comparison and a non-self genome comparison which is estimated to take about 9.4 days with the above resources. With the advent of personalized genome sequencing project, it would be desirable to compare 100s of individuals genome with a reference genome. This would involve a non-self genome and a self genome comparison for each genome, and would take around 7 days for each individuals genome using GMSECT and the above mentioned resources.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Singh, A.. 2021-03-02. GMSECT: Genome-Wide Massive Sequence Exhaustive Com-parison Tool for structural and copy number variations. https://doi.org/10.1101/2021.03.01.433223

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗
bioRxiv2026

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗
bioRxiv2026

Contrastive Learning Improves Generalization on M2OR Olfactory Binding Dataset

Olfaction-based biosensors stand to have profound impacts in fields such as medical diagnostics, national security, and drug discovery. A promising mode of vapor phase biodetection mimics volatile organic compound-olfactory receptor (VOC-OR) interactions with target-specific protein or peptide probes; however, lack of experimental coverage remains a core bottleneck. Current computational approaches aim to improve prediction of VOC-OR binding to reduce experimental burden, and current machine learning approaches fail to demonstrate a level of generalization that addresses current engineering challenges. Here, using the Molecular to Olfactory Dataset (M2OR) and Insect Olfactory Binding Protein Database (iOBPdb) datasets, data partitioning methods and their performance are evaluated on a neural network and contrastive learning framework on such VOC-OR binding datasets, varying: model training, architecture, and structure of the input data distribution. It was found that holding out certain chemical or proteomic motifs from the training data diminished model performance with a two layer neural network model. The model was able to generalize to state of the art performance (0.88 AUCROC) on every data split by applying a contrastive learning objective defined by shared molecular constituents and a stochastic sampler. This suggests the contrastive objective provides a strong inductive bias for neural networks to predict novel VOC-OR pairs. These findings highlight that model performance on pairwise biological interaction datasets can be inflated through identity leakage from constituent overlap, and emphasize the need for more robust evaluation protocols. Furthermore, we show contrastive regularization mitigates dataset degeneracy through enforcement of compatibility in the models latent geometry that may extend to other binder target-datasets where identity leakage confounds evaluation.

bioinformatics↗