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Record W4386545312 · doi:10.1101/2023.09.07.556366

An imputed ancestral reference genome for the <i>Mycobacterium tuberculosis</i> complex better captures structural genomic diversity for reference-based alignment workflows

2023· preprint· en· W4386545312 on OpenAlexafffund
Luke B. Harrison, Vivek Kapur, Marcel A. Behr

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersHuck Institutes of the Life SciencesAlliance de recherche numérique du CanadaDepartment for International DevelopmentBill and Melinda Gates FoundationU.S. Department of Agriculture
KeywordsMycobacterium tuberculosis complexReference genomeBiologyGenomeSequence (biology)Whole genome sequencingSequence analysisGeneticsComputational biologyMycobacterium tuberculosisTuberculosisGene

Abstract

fetched live from OpenAlex

Abstract Reference-based alignment of short-reads is a widely used technique in genomic analysis of the Mycobacterium tuberculosis complex (MTBC) and the choice of reference sequence impacts the interpretation of analyses. The most widely used reference genomes include the ATCC type strain (H37Rv) and the putative MTBC ancestral sequence of Comas et al . both of which are based on a lineage 4 sequence. As such, these referents do not capture the complete structural variation now known to be present in the MTBC. To better represent the base of the MTBC, we generated an imputed ancestral genomic sequence, termed MTBC 0 from reference-free alignments of closed MTBC genomes. When used as a reference sequence in alignment workflows, MTBC 0 mapped more short sequencing reads and called more SNPs relative to the Comas et al. sequence while exhibiting minimal impact on the overall phylogeny of MTBC. The results also show that MTBC 0 provides greater fidelity in capturing genomic variation and allows for the inclusion of regions absent in H37Rv such as the TbD1 and RvD4496/RD7/RD713 regions in standard MTBC workflows without additional steps. The use of MTBC 0 as an ancestral reference sequence into a standard workflows modestly improved read mapping, SNP calling and intuitively facilitates the study of structural variation and evolution in MTBC. Data Summary The MTBC 0 sequence, is available in the online data supplement in FASTA format at https://github.com/lukebharrison/MTBC0 . Included with the MTBC 0 sequence in the data supplement are: the reference-free alignment of MTBC closed genomes in hierarchical alignment (HAL) format, control files for cactus, annotations for H37Rv and L8, a BED file of regions excluded from SNP calls lifted over onto MTBC 0 , as well as the scripts used to call SNPs and the phylogenetic trees generated in this article. All previously published sequence data is available at the NCBI nucleotide and SRA databases, accession number for sequences used in this manuscript are available in Supplementary Tables 1 and 2. Impact Statement This article describes an imputed ancestral genomic sequence (MTBC 0 ) at the base of the MTBC for use as a reference sequence for Mycobacterium tuberculosis genomic workflows. Widely used reference sequences are limited to the structural diversity present in H37Rv, a lineage 4 isolate. MTBC 0 obviates this limitation by incorporating the structural variation present at the base of the Mycobacterium tuberculosis complex (MTBC) by encompassing a wide sample of human and animal lineages including newly discovered lineages (L8, M. orygis ). Use of MTBC 0 enables the mapping of more reads and calling of more SNPs and allows for the investigation of structural variation not present in the current used reference sequences within this important group of animal and human pathogens.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.250
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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