Biology subjects

Nafees, S.

Publications and source records attributed to Nafees, S..

4 recordsLinked to original sources

bioRxiv2022

ortho_seqs: A Python tool for sequence analysis and higher order sequence-phenotype mapping

MotivationAn important goal in sequence analysis is to understand how parts of DNA, RNA, or protein sequences interact with each other and to predict how these interactions result in given phenotypes. Mapping phenotypes onto underlying sequence space at first- and higher order levels in order to independently quantify the impact of given nucleotides or residues along a sequence is critical to understanding sequence-phenotype relationships. ResultsWe developed a Python software tool, ortho_seqs, that quantifies higher order sequence-phenotype interactions based on our previously published method of applying multivariate tensor-based orthogonal polynomials to biological sequences. Using this method, nucleotide or amino acid sequence information is converted to vectors, which are then used to build and compute the first- and higher order tensor-based orthogonal polynomials. We derived a more complete version of the mathematical method that includes projections that not only quantify effects of given nucleotides at a particular site, but also identify the effects of nucleotide substitutions. We show proof of concept of this method, provide a use case example as applied to synthetic antibody sequences, and demonstrate the application of ortho_seqs to other other sequence-phenotype datasets. Availabilityhttps://github.com/snafees/ortho_seqs & documentation https://ortho-seqs.readthedocs.io/

bioinformatics↗

bioRxiv2022

AIRRscape: an interactive tool for exploring B-cell receptor repertoires and antibody responses

The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires. Author SummaryTechnological advances in next generation sequencing have allowed for broad experimental sampling of immune repertoires, providing insight into how our immune system responds to infection, vaccination, autoimmunity, and cancer. The scale of these "big data", however, make it difficult to bioinformatically extract the key sequence features that are shared across multiple repertoires. With AIRRscape, we enable large-scale immune repertoire visualization and analysis that requires no knowledge of the command line or advanced programming. By providing the community with an open-source, interactive, and user-friendly interface, we reduce the barriers to exploring immune repertoires at scale. We demonstrate the use of AIRRscape to characterize features of immune responses to viral infection that are shared across multiple repertoire datasets.

immunology↗

bioRxiv2021

Single-cell transcriptomics for the 99.9% of species without reference genomes

Single-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on Earth, >99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of "k-mer homology," combining biochemical synonyms in degenerate protein alphabets with uniform data subsampling via MinHash into a pipeline called Kmermaid. Implementing this pipeline enables direct detection of similar cell types across species from transcriptomic data without the need for a reference genome. Underpinning Kmermaid is the tool Orpheum, a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. After validating Kmermaid using datasets from human and mouse lung, we applied Kmermaid to the Chinese horseshoe bat (Rhinolophus sinicus), where we propagated cellular compartment labels at high fidelity. Our pipeline provides a high-throughput tool that enables analyses of transcriptomic data across divergent species transcriptomes in a genome- and gene annotation-agnostic manner. Thus, the combination of Kmermaid and Orpheum identifies cell type-specific sequences that may be missing from genome annotations and empowers molecular cellular phenotyping for novel model organisms and species.

bioinformatics↗

bioRxiv2020

Analyzing Genomic Data Using Tensor-Based Orthogonal Polynomials

An important goal in molecular biology is to quantify both the patterns across a genomic sequence and the relationship between phenotype and underlying sequence. We propose a multivariate tensor-based orthogonal polynomial approach to characterize nucleotides or amino acids in a given sequence and map corresponding phenotypes onto the sequence space. We have applied this method to a previously published case of small transcription activating RNAs (STARs). Covariance patterns along the sequence showcased strong correlations between nucleotides at the ends of the sequence. However, when the phenotype is projected onto the sequence space, this pattern doesnt emerge. When doing second order analysis and quantifying the functional relationship between the phenotype and pairs of sites along the sequence, we identified sites with high regressions spread across the sequence, indicating potential intramolecular binding. In addition to quantifying interactions between different parts of a sequence, the method quantifies sequence-phenotype interactions at first and higher order levels. We discuss the strengths and constraints of the method and compare it to computational methods such as machine learning approaches. An accompanying command line tool to compute these polynomials is provided. We show proof of concept of this approach and demonstrate its potential application to other biological systems.

bioinformatics↗