Biology subjects

Ji, H. J.

Publications and source records attributed to Ji, H. J..

6 recordsLinked to original sources

bioRxiv2025

Isoswitching drives the aging process in human brains

Learning, reasoning, and working memory functions are attributed to the dorsolateral prefrontal cortex (DLPFC), a brain region that is highly evolved in primates and notably variable among individuals. Environmental and genetic factors likely contribute to this variability, but little is known about how they influence changes within an individual brain across the lifespan as different cognitive tasks and challenges arise. Most genetic studies focus on DNA mutations or changes in overall gene expression levels. However, genes can also alter the form in which they are expressed through alternative splicing. Using RNA-seq data from prenatal and postnatal human DLPFCs, we observed that many genes undergo a dramatic rewiring of their isoform usage around the time of birth. Further, thousands of genes continue to undergo gradual, temporally regulated shifts in their preferred isoforms, a phenomenon we term isoswitching. We present isoswitching as a major force in brain development and aging, capable of accurately predicting subject age from prenatal stages through adulthood and beyond eighty years of age. This study represents the first human brain age prediction based solely on RNA-seq data, establishing a molecular framework for understanding normative brain development and aging. We also report isoswitching in the brain of a closely related primate, the rhesus macaque.

genomics↗

bioRxiv2025

TD2: finding protein coding regions in transcripts

The transcriptome encompasses all RNA transcripts in eukaryotic cells, orchestrating gene expression and regulating cellular function, development, and adaptation. Identifying open reading frames (ORFs) in transcripts is a critical step in transcriptome analysis. We introduce TD2, a new tool for ab initio annotation of protein-coding ORFs in transcripts. We find TD2 to be sensitive and precise when compared to other state-of-the-art tools in reference transcripts and transcriptome assemblies from a diverse array of eukaryotes. TD2 is available at https://github.com/Markusjsommer/TD2. The project is open-source, developed in Python with PyTorch, and is freely available to all academic, government, and commercial users under the MIT license.

genomics↗

bioRxiv2025

Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling

The accuracy of spatial gene expression profiles generated by probe-based in situ spatially-resolved transcriptomic technologies depends on the specificity with which probes bind to their intended target gene. Off-target binding, defined as a probe binding to something other than the target gene, can distort a genes true expression profile, making probe specificity essential for reliable transcriptomics. Here, we investigated off-target binding affecting the 10x Genomics Xenium technology. We developed a software tool, Off-target Probe Tracker (OPT), to identify putative off-target binding via alignment of probe sequences and assessing whether mapped loci corresponded to the intended target gene across multiple reference annotations. Applying OPT to a Xenium human breast gene panel, we identified at least 14 out of the 313 genes in the panel potentially impacted by off-target binding to protein-coding genes. To substantiate our predictions, we leveraged a Xenium breast cancer dataset generated using this gene panel and compared results to orthogonal spatial and single-cell transcriptomic profiles from Visium CytAssist and 3' single-cell RNA-seq derived from the same tumor block. Our findings indicate that for some genes, the expression patterns detected by Xenium demonstrably reflect the aggregate expression of the target and predicted off-target genes based on Visium and single-cell RNA-seq rather than the target gene alone. We further applied OPT to identify potential off-target binding in custom gene panels and integrate tissue-specific RNA-seq data to assess effects. Overall, this work enhances the biological interpretability of spatial transcriptomics data and improves reproducibility in spatial transcriptomics research.

bioinformatics↗

bioRxiv2024

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

Recently developed long-read RNA sequencing technologies promise to provide a more accurate and comprehensive view of transcriptomes compared to short-read sequencers, primarily due to their capability to achieve full-length sequencing of transcripts. However, realizing this potential requires computational tools tailored to process long reads, which exhibit a higher error rate than short reads. Existing methods for assembling and quantifying long-read data often disagree on expressed transcripts and their abundance levels, leading researchers to lack confidence in the transcriptomes produced using this data. One approach to address the uncertainties in transcriptome assembly and quantification is by assigning the long reads to transcripts, enabling a more detailed characterization of transcript support at the read level. Here, we introduce TranSigner, a versatile tool that assigns long reads to any input transcriptome. TranSigner consists of three consecutive modules performing: read alignment to the given transcripts, computation of read-to-transcript compatibility based on alignment scores and positions, and execution of an expectation-maximization algorithm to probabilistically assign reads to transcripts and estimate transcript abundances. Using simulated data and experimental datasets from three well-studied organisms -- Homo sapiens, Arabidopsis thaliana, and Mus musculus -- we demonstrate that TranSigner achieves accurate read assignments, obtaining higher accuracy in transcript abundance estimation compared to existing tools.

genomics↗

bioRxiv2024

Upstream open reading frames may contain hundreds of novel human exons

Several recent studies have presented evidence that the human gene catalogue should be expanded to include thousands of short open reading frames (ORFs) appearing upstream or downstream of existing protein-coding genes, each of which would comprise an additional bicistronic transcript in humans. Here we explore an alternative hypothesis that would explain the translational and evolutionary evidence for these upstream ORFs without the need to create novel genes or bicistronic transcripts. We examined 2,199 upstream ORFs that have been proposed as high-quality candidates for novel genes, to determine if they could instead represent protein-coding exons that can be added to existing genes. We checked for the conservation of these ORFs in four recently sequenced, high-quality human genomes, and found a large majority (87.8%) to be conserved in all four as expected. We then looked for splicing evidence that would connect each upstream ORF to the downstream protein-coding gene at the same locus, thus creating a novel splicing variant using the upstream ORF as its first exon. These protein coding exon candidates were further evaluated using protein structure predictions of the protein sequences that included the proposed new exons. We determined that 582 out of 2,199 upstream ORFs have strong evidence that they can form protein coding exons that are part of an existing gene, and that the resulting protein is predicted to have similar or better structural quality than the currently annotated isoform. Author SummaryWe analyzed over 2000 human sequences that have been proposed to represent novel protein-coding genes, and that reside just upstream of known genes. These "upstream ORFs" (uORFs) would represent a surprisingly large addition to the human gene catalogue, which after decades of refinement now contains just under 20,000 protein-coding genes. They would also create over 2000 new bicistronic genes, which number only 10 in current human annotation databases. We hypothesized that rather than novel genes, these sequences might instead represent novel exons that can be spliced into existing protein-coding genes, creating new isoforms of those genes. Using a combination of transcriptional evidence and computational predictions, we show that at least 582 of the previously-described uORFs can be used to create novel protein-coding exons, generating new transcripts and new protein isoforms, but not requiring the addition of entirely new genes to the human gene catalogue. We also demonstrate that the predicted three-dimensional structure of some of the new protein isoforms hints at new or improved functions for existing proteins.

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↗