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Bioinformatics

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bioRxiv2019

Bioinformatics Pipeline using JUDI: Just Do It

Large-scale data analysis in Bioinformatics requires executing several software in a pipelined fashion. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g., Snakemake and Nextflow, have been developed to ensure in case of multiple invocation of the pipeline, only the bare minimum stages that are affected by the changes across invocations get executed. However, when the pipeline needs to be executed with different settings of parameters, e.g., thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution of the pipeline. We developed JUDI on top of a Python based WMS, DoIt, for a systematic handling of pipeline parameter settings based on the principles of DBMS that simplifies plug-and-play scripting. The effectiveness of JUDI is demonstrated in a pipeline for analyzing large scale HT-SELEX data for transcription factor and DNA binding where JUDI reduces scripting by a factor of five.\n\nAvailabilityhttps://github.com/ncbi/JUDI

bioinformatics↗

bioRxiv2019

A flexible pipeline combining bioinformatic correction tools for prokaryotic and eukaryotic metabarcoding

Environmental metabarcoding is an increasingly popular tool for studying biodiversity in marine and terrestrial biomes. With sequencing costs decreasing, multiple-marker metabarcoding, spanning several branches of the tree of life, is becoming more accessible. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on Illumina read correction with DADA2 allowing analyzing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster Amplicon Sequence Variants (ASVs) into Operational Taxonomic Units (OTUs) with swarm v2, a network-based clustering algorithm, and to further curate the ASVs/OTUs based on sequence similarity and co-occurrence rates using a recently developed algorithm, LULU. Finally, flexible taxonomic assignment was implemented via Ribosomal Database Project (RDP) Bayesian classifier and BLAST. We validate this pipeline with ribosomal and mitochondrial markers using eukaryotic mock communities and 42 deep-sea sediment samples. The results show that ASVs, reflecting genetic diversity, may not be appropriate for alpha diversity estimation of organisms fitting the biological species concept. The results underline the advantages of clustering and LULU-curation for producing more reliable metazoan biodiversity inventories, and show that LULU is an effective tool for filtering metazoan molecular clusters, although the minimum identity threshold applied to co-occurring OTUs has to be increased for 18S. The comparison of BLAST and the RDP Classifier underlined the potential of the latter to deliver very good assignments, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific, databases adapted to the studied communities.

bioinformatics↗

bioRxiv2019

A Simplest Bioinformatics Pipeline for Whole Transcriptome Sequencing: Overview of the Processing and Steps from Raw Data to Downstream Analysis.

Recent advances in next generation sequencing (NGS) technologies have heralded the genomic research. From the good-old inferring differentially expressed genes (DEG) using microarray to the current adage NGS-based whole transcriptome or RNA-Seq pipelines, there have been advances and improvements. With several bioinformatics pipelines for analysing RNA-Seq on rise, inferring the candidate DEGs prove to be a cumbersome approach as one may have to reach consensus among all the pipelines. To Check this, we have benchmarked the well known cufflinks-cuffdiff pipeline on a set of datasets and outline it in the form of a protocol where researchers interested in performing whole transcriptome shotgun sequencing and its downstream analysis can better disseminate the analysis using their datasets.

bioinformatics↗

bioRxiv2025

Integrative metagenomics and structural bioinformatics identify explainable gut microbial variants associated with Crohns disease

Metagenomics has revealed disease-associated shifts in microbial taxa and functions in inflammatory bowel disease (IBD) patients. However, the role of genomic variation in gut commensals remains poorly understood. Here, we integrated metagenomic profiling, variant calling, and structural bioinformatics to identify disease-associated variants in the gut microbes. Crohns disease (CD) and ulcerative colitis (UC) showed significant negative associations with Bacteroides uniformis, Bacteroides vulgatus, and Eubacterium rectale. These bacteria exhibited 190,712 single-nucleotide polymorphisms, including 479 CD-specific and 235 UC-specific variants. Variant prioritization identified a CD-specific Val170Leu substitution in the conserved starch-binding domain of the Starch Utilization System D (SusD) protein in B. uniformis. Structural modeling and cyclodextrin docking indicated reduced binding affinity in the mutant, while 200-ns molecular dynamics simulations showed stable ligand retention only in the wild type. These findings suggest that impaired starch metabolism driven by SusD variation may contribute to B. uniformis depletion in CD and demonstrate the value of integrating metagenomics with structural analyses to identify functionally relevant microbial variants.

bioinformatics↗

bioRxiv2026

NanoHIVSeq: A Long-Read Bioinformatics Pipeline for High-Throughput Processing of HIV Env Sequences

High-throughput sequencing of the HIV-1 envelope (Env) gene from viral quasispecies is essential for epidemiology, virus-antibody coevolution studies, and evaluating therapeutics, but the conventional single-genome amplification (SGA) coupled with Sanger sequencing is labor-intensive and low-throughput. Oxford Nanopore Technology (ONT) offers long-read sequencing advantages, but high error rates (1-7%) poses a challenge in identifying biological variants from sequencing artifacts. Without unique molecular identifiers (UMIs), which lose DNA template and add complexity in library preparation, here we introduce NanoHIVSeq, a UMI-free and reference-free bioinformatics pipeline that processes ONT data from bulk Env PCR amplicons through multistep clustering, consensus polishing, indel correction, denoising, and genotyping to recover functional full-length Env variants. By leveraging advanced ONT duplex sequencing technology, NanoHIVSeq was assessed using plasmid env and bulk HIV reservoir datasets, demonstrating high robustness, recovery rate, reproducibility, and accuracy (>99.9% or >Q30) comparable to UMI approaches. Our findings indicated that NanoHIVSeq allows flexible and simplified ONT library preparation for reproducible and efficient Env sequencing especially for large cohorts.

bioinformatics↗

bioRxiv2026

Benchmarking Agentic Bioinformatics Systems for Complex Protein-Set Retrieval: A Coccolithophore Calcification Case Study

Large language model agents are increasingly used for bioinformatics tasks that require external databases, tool use, and long multi-step retrieval workflows. However, practical evaluation of these systems remains limited, especially for prompts whose target set is both large and biologically heterogeneous. Here, I benchmarked three agent systems on the same difficult retrieval task: downloading coccolithophore calcification-related proteins from UniProt across six mechanistically distinct categories, while producing category-separated FASTA files and supporting evidence. The compared systems were Codex app agents extended with Claude Scientific Skills, Biomni Lab online, and DeerFlow 2 with default skills only. Outputs were normalized at the UniProt accession level and compared category by category using overlap analysis, Venn decomposition, and a heuristic relevance assessment of each subset relative to the benchmark prompt. Across the six shared categories, Codex retrieved 2,118 proteins, DeerFlow 6,255, and Biomni 8,752 in a run. Codex showed the best balance between sensitivity and specificity: 92.4% of its proteins fell into subsets labeled high relevance and the remaining 7.6% into medium relevance. DeerFlow was substantially more exhaustive, but 43.8% of its proteins fell into low or low-medium relevance subsets. Biomni produced the largest sets, yet 69.5% of its proteins fell into low or low-medium relevance subsets, mainly due to broad expansion into generic calcium sensors, kinases, transcription factors, and poorly specific domain families. Category-specific analysis showed that Codex was the strongest primary source for inorganic carbon transport, calcium and pH regulation, vesicle trafficking, and signaling, whereas DeerFlow contributed valuable complementary matrix and polysaccharide candidates. A second run for each system also separated them strongly by repeatability: Codex had the highest within-system stability (mean category Jaccard 0.982; micro-Jaccard 0.974), DeerFlow was intermediate (0.795; 0.571), and Biomni was least stable (0.412; 0.319). These results suggest that for complex protein-family retrieval tasks, agent quality depends less on raw output volume than on prompt decomposition, taxonomic scoping, exact query generation, provenance-rich export artifacts, and repeated-run stability.

bioinformatics↗

bioRxiv2026

oxo-flow: compiled, memory-safe bioinformatics workflow orchestration

Bioinformatics analyses depend on workflow engines to coordinate dozens of computational tools across complex dependency chains. The most widely adopted engines--Snakemake, Nextflow, the Common Workflow Language (CWL), and the Workflow Description Language (WDL)--run on interpreted or just-in-time (JIT) compiled language runtimes, incurring hundreds of milliseconds of startup latency and providing no compile-time safety guarantees from the host language. We developed oxo-flow, a workflow engine written in Rust that compiles to a single native binary. On an Apple M5 processor, oxo-flow parses, validates, and dry-runs a production-scale workflow in roughly 22 milliseconds--before Snakemake or Nextflow have finished loading their runtime environments. Peak memory usage is 16 megabytes, representing six- to seven-fold reductions relative to Snakemake and Nextflow. Dry-run latency is essentially independent of workflow size: a hundred-fold increase in rule count adds approximately 0.4 milliseconds. oxo-flow integrates 31 command-line tools, a REST interface with 60 endpoints, an embedded web application, and native cluster submission into a single 10-megabyte binary. It provides per-rule environment isolation across seven backends, checkpoint-based fault tolerance with cryptographic output verification, and a formal installation and operational qualification protocol for regulated laboratory environments. Ten curated workflows and three demonstration pipeline repositories are available. oxo-flow is freely available under Apache License 2.0 at https://github.com/Traitome/oxo-flow.

bioinformatics↗

bioRxiv2026

Structure Bioinformatics of Eight Human ATP Synthase Fo Subunits and Their AlphaFold3-Predicted Water-Soluble QTY Analogs

Human mitochondrial ATP synthase is an essential rotary motor enzyme that produces most of the cellular ATP through oxidative phosphorylation. Its membrane-embedded Fo sector contains highly hydrophobic transmembrane subunits that are challenging to study in aqueous environments without detergents. This study explores whether applying the QTY code can reduce the hydrophobicity of selected ATP synthase Fo subunits while preserving their overall molecular structures. We applied the QTY code to eight human ATP synthase Fo subunits: ATP6, ATP8, ATPK, ATP68, ATPMK, AT5G1, AT5G2, and AT5G3. Hydrophobic amino acids leucine (L), isoleucine (I), valine (V), and phenylalanine (F) in transmembrane regions were systematically replaced with hydrophilic glutamine (Q), threonine (T), and tyrosine (Y). Four native subunits with available CryoEM structures from human ATP synthase (PDB: 8H9S) were superposed with their AlphaFold3-predicted QTY analogs. The native ATP synthase Fo subunits superposed well with their respective QTY analogs. For the CryoEM-native comparisons, RMSD values ranged from 0.565[A] to 2.546[A]. For the AlphaFold3-native comparisons of subunits without CryoEM structures, RMSD values ranged from 0.204[A] to 0.297[A]. Despite substantial QTY substitutions in the transmembrane regions, ranging from 38.89% to 50.79%, the QTY analogs retained similar overall folds, molecular weights, and isoelectric points. Hydrophobic surface analysis showed that the QTY analogs had reduced hydrophobic patches compared with their native counterparts, with average hydrophobicity decreasing from 0.2959 in native proteins to -1.1023 in QTY analogs. These structural bioinformatics studies suggest that the QTY code can be applied to ATP synthase Fo subunits to generate more hydrophilic, potentially water-soluble analogs while preserving overall structural similarity. These results extend the application of the QTY code to the membrane-embedded Fo sector of ATP synthase and provide a foundation for future experimental studies testing whether these QTY analogs can be expressed, purified, and evaluated for assembly or proton-transfer-related functions.

bioinformatics↗

bioRxiv2026

Integrated Bioinformatics Analysis of PALB2 Reveals Expression Patterns, Molecular Interactions, and Prognostic Significance in Breast Cancer

BackgroundPartner and Localizer of BRCA2 (PALB2) is a key tumor suppressor gene involved in homologous recombination-mediated DNA repair through its interactions with BRCA1 and BRCA2. Germline alterations in PALB2 have been associated with hereditary breast cancer risk; however, its broader molecular role in breast cancer progression and prognosis requires further investigation. MethodsA comprehensive in-silico analysis of PALB2 was performed using publicly available databases and bioinformatics platforms. Differential expression of PALB2 in breast cancer were evaluated using GEPIA2. Prognostic significance was assessed through Kaplan-Meier analyses for overall survival (OS) and disease-free survival (DFS). Protein-protein interaction (PPI) networks were constructed using STRING. Functional enrichment analyses of PALB2-associated genes were conducted using g. Mutational profiling of PALB2 in breast cancer was performed using cBioPortal with data from TCGA breast cancer cohorts. ResultsPALB2 expression was elevated in breast tumor tissues compared with normal breast tissues. Survival analyses revealed no statistically significant association between PALB2 expression and either overall survival (HR = 0.88, p = 0.44) or disease-free survival (HR = 0.74, p = 0.11). Protein interaction analysis revealed strong interactions between PALB2 and major DNA repair proteins including BRCA1, BRCA2, RAD51, RAD51C, FANCD2, and BRIP1. Functional enrichment analysis showed limited significant pathway enrichment, with only marginal transcription factor motif enrichment observed. Mutational analysis demonstrated diverse genomic alterations including missense mutations, truncating mutations, copy number gains, and shallow deletions. ConclusionThe findings support the biological relevance of PALB2 in breast cancer through its elevated expression and strong connectivity within DNA repair pathways. However, PALB2 expression alone does not appear to serve as an independent prognostic indicator. Further studies integrating genomic, transcriptomic, and clinical parameters are required to clarify its role in breast cancer progression and therapeutic response.

bioinformatics↗

bioRxiv2026

EXCAVATE-HT: A Bioinformatic Pipeline to Identify Targetable Genomic Variants for Allele-Specific Editing

Allele-specific CRISPR/Cas editing is a powerful tool with great potential for treating genetic diseases and for uncovering the effects of allelic diversity. By targeting commonly inherited single nucleotide polymorphisms (SNPs), a small number of gRNAs can treat many more individuals than targeting rare disease mutations. However, current tools for identifying common targetable variants and generating CRISPR guide RNAs (gRNA) have fundamental conceptual and technical limitations. Here, we introduce EXCAVATE-HT (EXtracting Common Allelic VAriants for Targeted Editing in High-Throughput) a bioinformatic tool that mines population variant data to generate CRISPR libraries targeting genomic loci for allele-specific editing. Users define their loci of interest, Cas species, and SNP frequency, then EXCAVATE-HT outputs an annotated list of allele-specific gRNAs. EXCAVATE-HT can also generate libraries of gRNA pairs to enable excision. We illustrate the use of EXCAVATE-HT to design and characterize multiple gRNA libraries for allele-specific targeting of the disease gene, Cone-Rod Homeobox (CRX). EXCAVATE-HT revealed multiple excisions that could treat >30-fold more patients than targeting a single CRX disease mutation.

bioinformatics↗

bioRxiv2026

Mandatory use of mock communities highlighted by the descriptive comparison of Epi2Me 16S and EMU bioinformatic workflows for full-length 16S rRNA Nanopore sequencing.

Full-length 16S rRNA gene sequencing using Oxford Nanopore Technologies has emerged as a promising approach to improve species-level resolution in microbiota studies. However, the accuracy of taxonomic assignment remains highly dependent on the bioinformatics s used to process Nanopore long-read data. Therefore, the only way to ensure a good level of certainty in obtained results is to use positive controls in the form of mock communities in the experimental designs. In this study, we compared the performance of Epi2Me 16S (using Minimap2 or Kraken2) workflows provided by Oxford Nanopore Technologies and an EMU workflow for full-length 16S rRNA gene analysis. Using a commercial mock community sequenced across multiple Nanopore runs, taxonomic assignment accuracy and reproducibility was evaluated. Epi2Me-Kraken2 exhibited 18 % of incorrect genus-level assignments and failed to identify 3 species present in the mock community. While Epi2Me-Minimap2 achieved an excellent genus-level classification, reporting 9 % of sequences assigned to a genus not in the mock community, species-level assignments were inconsistent for several community members such as Listeria. In contrast, EMU provided accurate and consistent species-level taxonomic profiles, with all species correctly identified while keeping the number of genus absent from the mock community at 1.2%. ImportanceThese results highlight that Epi2Me integrated workflows are not the best option for specie-level taxonomic assignation. More importantly, this paper underscores the importance of routine inclusion of positive controls for microbiota studies, in the form of mock communities, as a critical safeguard for accurate data interpretation. Without the use of a mock community, a paper published would be at risk of reporting wrong observations and inaccurate conclusions.

bioinformatics↗

bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

bioRxiv2026

DIRECTION-AWARE INTEGRATED BIOINFORMATICS ANALYSIS REVEALS CONCORDANT AND DISCORDANT MOLECULAR SIGNATURES LINKING TYPE 2 DIABETES MELLITUS AND POLYCYSTIC OVARY SYNDROME

Abstract Polycystic ovarian syndrome and Type 2 diabetes mellitus are complex multivariate diseases that share well-established clinical pathophysiology, yet the molecular basis of their overlap is unknown. Prior bioinformatics studies have identified common differentially expressed genes between these two conditions. However, they have not accounted for the directionality of the gene expression changes observed, potentially obscuring any biological distinctions they may have. This study applied a direction-aware approach to classify molecular signatures between PCOS and T2DM as concordant or discordant. Transcriptomic analysis of GEO datasets GSE138518 (ovarian granulosa tissue, PCOS) and GSE25724 (pancreatic islet tissue, T2DM) identified 225 and 1,302 DEGs, respectively. Venn diagram analysis showed that only three genes (SLC6A8, RGS4, and SORL1) were shared between PCOS and T2DM from the total of 1,527 genes, and all 3 genes were regulated in opposing directions. Disease gene retrieval from the Comparative Toxicogenomics Database, Online Mendelian Inheritance in Man database, and GeneCards showed 214 shared disease-associated genes, with 311 genes unique to PCOS and 496 genes unique to T2DM. Protein-protein interaction construction using STRING (version 12.0) identified 70 interacting nodes. CytoHubba analysis across 6 scoring methods identified 18 high-confidence hub genes, including INS, BCL2, MTOR, LEP, MFN2, and PIK3CD. Functional enrichment analysis identified biological processes including immune activation, apoptosis, and insulin signaling, which were confirmed as the main pathways in KEGG pathway analysis. These findings show that while PCOS and T2DM share limited DEGs, they share common pathogenic pathways.

bioinformatics↗

bioRxiv2014

Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis

BackgroundRNAseq provides remarkable power in the area of biomarkers discovery and disease stratification. The main technical steps affecting the results of RNAseq experiments are Library Sample Preparation (LSP) and Bioinformatics Analysis (BA). At the best of our knowledge, a comparative evaluation of the combined effect of LSP and BA was never considered and it might represent a valuable knowledge to optimize alternative splicing detection, which is a challenging task due to moderate fold change differences to be detected within a complex isoforms background.\n\nResultsDifferent LSPs (TruSeq unstranded/stranded, ScriptSeq, NuGEN) allow the detection of a large common set of isoforms. However, each LSP also detects a smaller set of isoforms, which are characterized both by lower coverage and lower FPKM than that observed for the common ones among LSPs. This characteristic is particularly critical in case of low input RNA NuGEN v2 LSP.\n\nThe effect on statistical detection of alternative splicing considering low input LSP (NuGEN v2) with respect to high input LSP (TruSeq) on statistical detection of alternative splicing was studied using a benchmark dataset, in which both synthetic reads and reads generated from high (TruSeq) and low input (NuGEN) LSPs were spiked-in. Statistical detection of alternative splicing (AltDE) was done using prototypes of BA for isoform-reconstruction (Cuffdiff) and exon-level analysis (DEXSeq). Exon-level analysis performs slightly better than isoform-reconstruction approach although at most only 50% of the spiked-in transcripts are detected. Both isoform-reconstruction and exon-level analysis performances improve by rising the number of input reads.\n\nConclusionData, derived from NuGEN v2, are not the ideal input for AltDE, specifically when exon-level approach is used. It is notable that ribosomal depletion, with respect to polyA+ selection, reduces the amount of coding mappable reads resulting detrimental in the case of AltDE. Furthermore, we observed that both isoform-reconstruction and exon-level analysis performances are strongly dependent on the number of input reads.

Genomics↗

bioRxiv2015

A Critical Review on the Use of Support Values in Tree Viewers and Bioinformatics Toolkits

Phylogenetic trees are routinely visualized to present and interpret the evolutionary relationships of species. Virtually all empirical evolutionary data studies contain a visualization of the inferred tree with branch support values. Ambiguous semantics in tree file formats can lead to erroneous tree visualizations and therefore to incorrect interpretations of phylogenetic analyses.\n\nHere, we discuss problems that can and do arise when displaying branch values on trees after re-rooting. Branch values are typically stored as node labels in the widely-used Newick tree format. However, such values are attributes of branches. Storing them as node labels can therefore yield errors when re-rooting trees. This depends on the mostly implicit semantics that tools deploy to interpret node labels.\n\nWe reviewed 10 tree viewers and 10 bioinformatics toolkits that can display and re-root trees. We found that 14 out of 20 of these tools do not permit users to select the semantics of node labels. Thus, unaware users might obtain incorrect results when rooting trees inferred by common phylogenetic inference programs. We illustrate such incorrect mappings for several test cases and real examples taken from the literature. This review has already led to improvements and workarounds in 8 of the tested tools. We suggest tools should provide an option that explicitly forces users to define the semantics of node labels.

Evolutionary Biology↗

bioRxiv2016

Omics and bioinformatics approaches to target boar taint

In livestock species, a rapid growth in high-throughput omics data has accelerated the pace of studies that target to dissect economically important traits to provide better quality animal products to consumers. In pig industries, young boars are generally castrated to remove boar taint, a phenotypic and inheritable trait well-known by an abnormally bad smell and taste in pork meat derived from some uncastrated male pigs. Existence of porcine reference genome made possible to catalogue genome-wide QTLs, candidate genes and biomarkers in associations with boar taint and other industrially significant traits in pigs. The aim of this paper to review the contribution of bioinformatics resources and omics technology in boar taint related studies. This paper also provides concise details about state-of-the-art sequencing technology.

Genomics↗

bioRxiv2016

A bioinformatic panel to interrogate thousands of ExAC variants with minor reference allele that are missed by conventional variant calling

In variation sites with minor reference alleles, overlooking the detection of homozygous reference genotypes results in inadequate identification of potential disease variants. Current variant calling practices miss these clinically relevant alleles warranting new approaches. More than 26,000 Eome Aggregation Consortium (ExAC) variants have a minor reference allele including 44 variants with known ClinVar disease alleles. We demonstrated how the current variant calling standards miss homozygous reference disease variants in these sites. We developed a bioinformatic panel that can be used to screen these variants using commonly available variant callers. We provide here a simple strategy to screen potential disease-causing variants when present in homozygous reference state.

genomics↗

bioRxiv2020

Comprehensive bioinformatic analysis of newly sequenced Turdoides affinis mitogenome reveals the persistence of translational efficiency and dominance of NADH dehydrogenase complex-I in electron transport system over Leiothrichidae family

Mitochondrial genome provides useful information about species with respect to its evolution and phylogenetics. We have taken the advantage of high throughput next-generation sequencing technique to sequence the complete mitogenome of Yellow-billed babbler (Turdoides affinis), a species endemic to Peninsular India and Sri Lanka. Both, reference-based and de-novo assemblies of mitogenome were performed and observed that de-novo assembled mitogenome was most appropriate. The complete mitogenome of yellow-billed babbler (assembled de-novo) was 17,671 bp in length with 53.2% AT composition. Thirteen protein-coding genes along with 2 rRNAs and 22 tRNAs were detected along with duplicated control regions. The arrangement pattern of these genes was found conserved among Leiothrichidae family mitogenomes. Downstream bioinformatics analysis revealed the effect of translational efficiency and purifying selection pressure over all the thirteen protein-coding genes in yellow-billed babbler mitogenome. Moreover, genetic distance and variation analysis indicated the dominance of NADH dehydrogenase complex-I in the electron transport system of T. affinis. Evolutionary analysis revealed the conserved nature of all the protein-coding genes across Leiothrichidae family mitogenomes. Our limited phylogenetics results suggest that T. affinis is closer to Garrulax.

genomics↗