Biology subjects

Mao, J.-F.

Publications and source records attributed to Mao, J.-F..

6 recordsLinked to original sources

bioRxiv2026

FastaGuard: FASTA preflight QC for modern bioinformatics pipelines

Summary: FastaGuard is a streaming, explainable FASTA preflight QC tool that validates assembly FASTA files, detects structural and composition red flags, and emits a versioned multi-format report with an explicit pipeline gate before expensive downstream analysis. We evaluated v1.1.0 in four analyses whose design was fixed before data collection. On 700 public NCBI assemblies the pipeline gate stopped 19.7% (1.9% weighted to the population), always on its high-N rule and mostly in scaffold-level genomes; the NCBI submission gate stopped 2.0%, and an independent recount confirmed every stop. Among 1,040 faults injected into real assemblies, the NCBI gate flagged 520 of 520 in 13 error classes (table2asn: 280 at error level) and blocked none of 120 benign changes. Across seven downstream tools, 46 of 182 defect cells failed and 12 altered the records without a warning, all in classes the pipeline gate blocks. Stopping a mismatched reference at the gate avoided about 54-62 s of mapping and sorting at a cost of 0.05-0.06 s. Availability and Implementation: Source code and documentation are freely available under the MIT license at https://github.com/ehsanestaji/FastaGuard (release tag v1.1.0: https://github.com/ehsanestaji/FastaGuard/releases/tag/v1.1.0; archived at doi:10.5281/zenodo.23728753). Install via Bioconda (mamba install -c conda-forge -c bioconda fastaguard=1.1.0), BioContainers, or GitHub release binaries. Implemented in Rust; supported on Linux and macOS, including Bioconda builds for linux-64, linux-aarch64, osx-64, and osx-arm64. Contact: ehsan.estaji@umu.se; jianfeng.mao@umu.se Supplementary information: Supplementary Note (designs, all results, deviations from the pre-registered protocol); scripts, manifests and result tables at Zenodo, doi:10.5281/zenodo.23729530.

bioinformatics↗

bioRxiv2026

VariantFlow: a selective-execution engine for efficient population genomic computation on large variant datasets

Population-scale sequencing now produces variant call sets with thousands of samples and millions of sites, making post-calling analysis a recurring bottleneck. Because the Variant Call Format stores every field of every record together, a tool answering a field-limited question still parses the unused annotations, FORMAT blocks, and per-sample values, which costs time without changing the result. We present VariantFlow, a command-line engine built on selective execution, decoding only the fields the current filter, statistic, or projection requires and preserving original records where possible. On correctness-matched benchmarks on the 1000 Genomes 3,202-sample high-coverage dataset, VariantFlow computed whole-chromosome missingness 8.0-8.5x faster than VCFtools at a constant 8.6 MB of memory, the chromosome 1 summary taking 128 s against 1031 s. An index-assisted FILTER=PASS predicate ran 123-273x faster than bcftools where block metadata excludes most of the file. Further gains, reported in Table 1, cover FORMAT-rich filtering on record subsets, linkage disequilibrium, and export-once Parquet queries served through DuckDB. Core population-genetic outputs were byte-identical to VCFtools, per-individual missingness and the site-frequency spectrum matched scikit-allel exactly, and the missing-data-aware pi and dxy estimators reproduced pixy's pairwise counts in every window. By decoding only the fields each operation requires, VariantFlow brings large-cohort post-calling analysis within reach of exploratory work on a single workstation. It complements rather than replaces bcftools, HTSlib, GATK, and VCFtools, and should be of value wherever large cohort VCFs are repeatedly summarised, filtered, or exported. VariantFlow is open source (Rust, MIT OR Apache-2.0) at https://github.com/ehsanestaji/VariantFlow; version 1.5.0 is archived at doi:10.5281/zenodo.21198172.

bioinformatics↗

bioRxiv2026

Relational biological structure improves fine-mapping of causal GWAS variants under weak signal

Linkage disequilibrium (LD) makes causal GWAS variants indistinguishable from correlated neighbours; resolving them is the fine-mapping problem, and the challenge is species-specific: humans face dense ancestry-imbalanced LD, yeast and Arabidopsis exceptionally long LD, and crop germplasm sparse and fragmented annotations that defeat human-biobank curation pipelines. Bayesian fine-mappers integrate annotations as flat per-variant priors, discarding the relational structure linking variants to tissue-specific eQTLs, pathways and protein-protein interactions. Hierarchical belief propagation (HBP) on a variant-gene-pathway factor graph matches Bayesian baselines at 5-40x speed; an annotation-adaptive complement, graph-augmented fine-mapping (GAFM), wins 27-2 against SuSiE at weak signal and recovers LDLR, APOE, LPL, GCKR and ANGPTL3 at single-variant resolution across four Pan-UK Biobank ancestries. On the 3,000 Rice Genomes grain weight + shape panel, mixture-prior posterior reweightings of GAFM/HBP and their ensemble (GAFM-MX, HBP-MX, ENS) reach 47.6% top-1-PIP exact-position recovery of 21 panel-matched stable QTNs -- the highest of any method, exceeding SuSiE (28.6%) and SBayesRC (14.3%) -- at 200-700x SuSiEs per-locus speed. Across 692 leads in four species, a non-uniform per-variant prior, not uniform high coverage, lets the graph break LD ties: adding a regulatory-element flag to an otherwise uniform human cache flips HBP narrower than GAFM from 0% to 88% on 321 Pan-UKB leads. These results recast multi-omics fine-mapping as a non-uniform-prior-curation problem rather than a uniform-coverage problem, and reframe post-GWAS analysis as message passing over biological structure rather than weighted regression on flattened annotations.

genomics↗

bioRxiv2026

GraphMana: graph-native data management for population genomics projects

Population genomics projects rely on fragmented file-based workflows that lose provenance and require full reprocessing when samples are added. Graph-Mana stores variant data in a graph database as packed genotype arrays with pre-computed population statistics, enabling incremental sample addition, provenance tracking, cohort management, and export to 17 formats. Two access paths serve different needs: a FAST PATH reading population-level arrays in O(K) time and a FULL PATH unpacking per-sample genotypes in O(N) time. On human 1000 Genomes data (3,202 samples, 70.7M variants), Graph-Mana completed a 46-operation lifecycle in 98 minutes from a single persistent database.

bioinformatics↗

bioRxiv2026

GraphPop: graph-native computation decouples population genomics complexity from sample count

Matrix-based population genomics tools scale as O(V x N ), re-reading the full genotype matrix for every analysis. Here we present GraphPop, a graph database engine that reduces summary statistic complexity to O(V x K) where K is population count--independent of sample count--by computing on pre-aggregated allele-count arrays stored as graph node properties. The same architecture enables annotation-conditioned queries via edge traversal, persistent analytical records, and multi-statistic composition. Applied to rice 3K (29.6M SNPs, 3,024 accessions) and human 1000 Genomes (3,202 samples, 22 autosomes), GraphPop reveals that all 12 rice subpopulations show{pi} N /{pi}S > 1.0, uncovers opposite consequence-level Fst regimes between species, and identifies KCNE1 as a candidate pre-Out-of-Africa sweep via convergence of five stored statistics. GraphPop achieves 146-327x query-time speedup for pre-aggregated statistics and 63-179x for bit-packed haplotype computation (iHS, XP-EHH, nSL), at constant[~] 160 MB procedure working memory. This complexity reduction makes systematic, annotation-integrated population genomics practical at scale.

bioinformatics↗

bioRxiv2020

Chromosome-scale assembly of the genome of Salix dunnii reveals a male-heterogametic sex determination system on chromosome 7

Sex determination systems in plants can involve either female or male heterogamety (ZW or XY, respectively). Here we used Illumina short reads, Oxford Nanopore Technologies (ONT) long reads, and Hi-C reads to assemble the first chromosome-scale genome of a female willow tree (Salix dunnii), and to predict genes using transcriptome sequences and available databases. The final genome sequence of 328 Mb in total was assembled in 29 contigs, and includes 31,501 genes. We inferred a male heterogametic sex determining factor on chromosome 7, suggesting that, unlike the female heterogamety of most species in the genus Salix, male heterogamety evolved in the subgenus Salix. The S. dunnii X-linked region occupies about 3.21 Mb of chromosome 7, and is probably in a pericentromeric region. Our data suggest that this region is enriched for transposable element insertions, and about one third of its 124 protein-coding genes were gained via duplications from other genome regions. We detect purifying selection on the genes that were ancestrally present in the region, though some have been lost. Transcriptome data from female and male individuals show more male- than female-biased genes in catkin and leaf tissues, and indicate enrichment for male-biased genes in the pseudo-autosomal regions. Our study provides valuable genomic resources for studying sex chromosome evolution in Salicaceae family.

genomics↗