Biology subjects

Chao, K.-H.

Publications and source records attributed to Chao, K.-H..

13 recordsLinked to original sources

bioRxiv2026

A unified framework for quantitative splicing and transcript prediction

RNA splicing is frequently disrupted by genetic variation, contributing to a wide range of human diseases. Identifying the variants responsible for altered splicing is therefore crucial but has long been challenging. Deep learning models that predict splicing from genomic sequence have substantially advanced their detection, but accurately resolving their quantitative effects on splicing and the resulting transcripts remains an unmet need. Here we introduce SpliceAI2, a deep learning model trained end-to-end on splice site, splice junction, and transcript usage across multiple mammalian species. SpliceAI2 reconstructs complete transcripts directly from genomic sequence and considerably improves identification of splice-altering variants and quantification of their effects in diverse benchmarks spanning cryptic splicing, splicing quantitative trait loci, and massively parallel reporter assays. Human population datasets provide independent validation of these predicted functional effects through evidence of natural selection and associated changes in plasma protein abundance. Application to a rare disease cohort reveals that predicted splice-altering variants are highly enriched in disease-associated genes and account for 15% of the excess genetic burden, with approximately half residing in deep intronic regions likely missed by exome sequencing. Together, these findings establish SpliceAI2 as a major step forward in interpreting splice-altering variants across the human genome.

genomics↗

bioRxiv2025

Predicting dynamic expression patterns in budding yeast with a fungal DNA language model

Predicting gene expression from DNA sequence remains challenging due to complex regulatory codes. We introduce a masked DNA language model pretrained on 165 fungal genomes closely related to budding yeast that captures conserved regulatory grammar. Fine-tuning the LM on yeast RNA-seq data--including high-resolution transcriptional regulator induction time courses generated in this study--yielded Shorkie, a model that substantially improves gene expression prediction compared to baselines trained without self-supervision. Shorkie identified canonical transcription factor (TF) binding motifs and tracked their usage across induction experiments. Furthermore, Shorkie accurately predicted variant effects, outperforming leading sequence-to-expression models in cis-eQTL classification and achieving high concordance with massively parallel reporter assays. Interpretability analyses revealed Shorkies ability to resolve promoter dynamics, splicing signals, and temporal changes in regulatory motif usage. This framework demonstrates that evolutionary-scale pretraining combined with transfer learning substantially improves our ability to decode gene regulation from sequence, providing insights into noncoding variants and regulatory networks.

genomics↗

bioRxiv2025

A complete diploid human genome benchmark for personalized genomics

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.

genomics↗

bioRxiv2025

OpenSpliceAI: An efficient, modular implementation of SpliceAI enabling easy retraining on non-human species

The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data. Here we introduce OpenSpliceAI, a trainable, open-source version of SpliceAI implemented in PyTorch to address these challenges. OpenSpliceAI supports both training from scratch and transfer learning, enabling seamless retraining on species-specific datasets and mitigating human-centric biases. Our experiments show that it achieves faster processing speeds and lower memory usage than the original SpliceAI code, allowing large-scale analyses of extensive genomic regions on a single GPU. Additionally, OpenSpliceAIs flexible architecture makes for easier integration with established machine learning ecosystems, simplifying the development of custom splicing models for different species and applications. We demonstrate that OpenSpliceAIs output is highly concordant with SpliceAI. In silico mutagenesis (ISM) analyses confirm that both models rely on similar sequence features, and calibration experiments demonstrate similar score probability estimates.

genomics↗

bioRxiv2024

Combining DNA and protein alignments to improve genome annotation with LiftOn

As the number and variety of assembled genomes continues to grow, the number of annotated genomes is falling behind, particularly for eukaryotes. DNA-based mapping tools help to address this challenge, but they are only able to transfer annotation between closely-related species. Here we introduce LiftOn, a homology-based software tool that integrates DNA and protein alignments to enhance the accuracy of genome-scale annotation and to allow mapping between relatively distant species. LiftOns protein-centric algorithm considers both types of alignments, chooses optimal open reading frames, resolves overlapping gene loci, and finds additional gene copies where they exist. LiftOn can reliably transfer annotation between genomes representing members of the same species, as we demonstrate on human, mouse, honey bee, rice, and Arabidopsis thaliana. It can further map annotation effectively across species pairs as far apart as mouse and rat or Drosophila melanogaster and D. erecta.

bioinformatics↗

bioRxiv2024

Clostridium innocuum, an opportunistic gut pathogen, inactivates host gut progesterone and arrests ovarian follicular development

HighlightsO_LIWe identified Clostridium innocuum as a key player in gut progesterone metabolism. C_LIO_LIProgesterone is converted into epipregnanolone with negligible progestogenic activity. C_LIO_LIWe identified the enzyme and mechanisms of microbial epipregnanolone production. C_LIO_LIC. innocuum caused decreased serum progesterone and follicular arrest in female mice. C_LIO_LIC. innocuum is a causal factor of progesterone resistance in women taking progesterone. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/585140v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@14cceaborg.highwire.dtl.DTLVardef@1946432org.highwire.dtl.DTLVardef@13e46d2org.highwire.dtl.DTLVardef@19b9a25_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG In briefChen et al. identified Clostridium innocuum as a major species involved in gut progesterone metabolism, with epipregnanolone as the main product, and elucidated the molecular mechanisms. C. innocuum inactivates gut progesterone in female mice, leading to decreased circulating progesterone levels. C. innocuum is also a causal factor of follicular arrest. Levels of progesterone, an endogenous female hormone, increase after ovulation; progesterone is crucial in the luteal phase to maintain successful pregnancy and prevent early miscarriage. Both endogenous and exogenous progesterone are recycled between the liver and gut; thus, the gut microbiota regulate host progesterone levels by inhibiting enterohepatic progesterone circulation. Our data indicated Clostridium innocuum as a major species involved in gut progesterone metabolism in women with infertility. C. innocuum converts progesterone into the neurosteroid epipregnanolone (with negligible progestogenic activity). We purified and characterized the corresponding enzyme, namely NADPH-dependent 5{beta}-dihydroprogesterone reductase, which is highly oxygen sensitive and whose corresponding genes are prevalent in C. innocuum. Moreover, C. innocuum-administered female C57BL/6 mice (aged 7 weeks) exhibited decreased serum progesterone levels ([~]35%). Clostridium-specific antibiotics (metronidazole) restored low serum progesterone levels in these mice. Furthermore, prolonged C. innocuum administration (12 weeks) arrested ovarian follicular development in female mice. Cytological and histological analyses indicated that C. innocuum may cause luteal phase insufficiency and affect menstrual regularity. Our findings suggest C. innocuum as a causal factor of progesterone resistance in women taking progesterone.

microbiology↗

bioRxiv2023

Splam: a deep-learning-based splice site predictor that improves spliced alignments

The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. Here we describe Splam, a novel method for predicting splice junctions in DNA based on deep residual convolutional neural networks. Unlike some previous models, Splam looks at a relatively limited window of 400 base pairs flanking each splice site, motivated by the observation that the biological process of splicing relies primarily on signals within this window. Additionally, Splam introduces the idea of training the network on donor and acceptor pairs together, based on the principal that the splicing machinery recognizes both ends of each intron at once. We compare Splams accuracy to recent state-of-the-art splice site prediction methods, particularly SpliceAI, another method that uses deep neural networks. Our results show that Splam is consistently more accurate than SpliceAI, with an overall accuracy of 96% at predicting human splice junctions. Splam generalizes even to non- human species, including distant ones like the flowering plant Arabidopsis thaliana. Finally, we demonstrate the use of Splam on a novel application: processing the spliced alignments of RNA-seq data in order to identify and eliminate errors. We show that when used in this manner, Splam yields substantial improvements in the accuracy of downstream transcriptome analysis on both poly(A) and ribo-depleted RNA-seq libraries. Overall, Splam offers a faster and more accurate approach to detecting splice junctions, while also providing a reliable and efficient solution for cleaning up erroneous spliced alignments.

genomics↗

bioRxiv2023

EASTR: Correcting systematic alignment errors in multi-exon genes

Accurate alignment of transcribed RNA to reference genomes is a critical step in the analysis of gene expression, which in turn has broad applications in biomedical research and in the basic sciences. We have discovered that widely used splice-aware aligners, such as STAR and HISAT2, can introduce erroneous spliced alignments between repeated sequences, leading to the inclusion of falsely spliced transcripts in RNA-seq experiments. In some cases, the "phantom" introns resulting from these errors have made their way into widely-used genome annotation databases. To address this issue, we have developed EASTR (Emending Alignments of Spliced Transcript Reads), a novel software tool that can detect and remove falsely spliced alignments or transcripts from alignment and annotation files. EASTR improves the accuracy of spliced alignments across diverse species, including human, maize, and Arabidopsis thaliana, by detecting sequence similarity between intron-flanking regions. We demonstrate that applying EASTR before transcript assembly substantially reduces false positive introns, exons, and transcripts, improving the overall accuracy of assembled transcripts. Additionally, we show that EASTRs application to reference annotation databases can detect and correct likely cases of mis-annotated transcripts.

genomics↗

bioRxiv2022

CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

The original CHESS database of human genes was assembled from nearly 10,000 RNA sequencing experiments in 53 human body sites produced by the Genotype-Tissue Expression (GTEx) project, and then augmented with genes from other databases to yield a comprehensive collection of protein-coding and noncoding transcripts. The construction of the new CHESS 3 database employed improved transcript assembly algorithms, a new machine learning classifier, and protein structure predictions to identify genes and transcripts likely to be functional and to eliminate those that appeared more likely to represent noise. The new catalog contains 41,356 genes on the GRCh38 reference human genome, of which 19,839 are protein-coding, and a total of 158,377 transcripts. These include 14,863 novel protein-coding transcripts. The total number of transcripts is substantially smaller than earlier versions due to improved transcriptome assembly methods and to a stricter protocol for filtering out noisy transcripts. Notably, CHESS 3 contains all of the transcripts in the MANE database, and at least one transcript corresponding to the vast majority of protein-coding genes in the RefSeq and GENCODE databases. CHESS 3 has also been mapped onto the complete CHM13 human genome, which gives a more-complete gene count of 43,773 genes and 19,968 protein-coding genes. The CHESS database is available at http://ccb.jhu.edu/chess.

genomics↗

bioRxiv2022

WGT: Tools and algorithms for recognizing, visualizing and generating Wheeler graphs

SummaryA Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check graphs that may have the Wheeler properties. Here we present Wheelie, an algorithm that combines a renaming heuristic with a permutation solver (Wheelie-PR) or a Satisfiability Modulo Theory (SMT) solver (Wheelie-SMT) to check whether a given graph has the Wheeler properties, a problem that is NP complete in general. Wheelie can check a variety of random and real-world graphs in far less time than any algorithm proposed to date. It can check a graph with 1,000s of nodes in seconds. We implement these algorithms together with complementary visualization tools in the WGT toolkit, available as open source software at https://github.com/Kuanhao-Chao/Wheeler_Graph_Toolkit.

bioinformatics↗

bioRxiv2022

The first gapless, reference-quality, fully annotated genome from a Southern Han Chinese individual

We used long-read DNA sequencing to assemble the genome of a Southern Han Chinese male. We organized the sequence into chromosomes and filled in gaps using the recently completed CHM13 genome as a guide, yielding a gap-free genome, Han1, containing 3,099,707,698 bases. Using the CHM13 annotation as a reference, we mapped all genes onto the Han1 genome and identified additional gene copies, generating a total of 60,708 genes, of which 20,003 are protein coding. A comprehensive comparison between the genes revealed that 235 protein-coding genes were substantially different between the individuals, with frameshifts or truncations affecting the protein-coding sequence. Most of these were heterozygous variants in which one gene copy was unaffected. This represents the first gene-level comparison between two finished, annotated individual human genomes.

bioinformatics↗

bioRxiv2020

sangeranalyseR: simple and interactive analysis of Sanger sequencing data in R

SummarysangeranalyseR is an interactive R/Bioconductor package and two associated Shiny applications designed for analysing Sanger sequencing from data from the ABIF file format in R. It allows users to go from loading reads to saving aligned contigs in a few lines of R code. sangeranalyseR provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, viewing chromatograms, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. sangeranalyseR comes with extensive online documentation, and outputs detailed interactive HTML reports. Availability and implementationsangeranalyseR is implemented in R and released under an MIT license. It is available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR) and on Github (https://github.com/roblanf/sangeranalyseR). Contactkuanhao.chao@gmail.com Supplementary informationDocumentation at https://sangeranalyser.readthedocs.io/.

bioinformatics↗

bioRxiv2019

RNASeqR: an R package for automated two-group RNA-Seq analysis workflow

RNA-Seq analysis has revolutionized researchers understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).

bioinformatics↗