bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/163436

Intragenic differential expression in archaea transcriptomes revealed by computational analysis of tiling microarrays

Khan, A.·Z. N. Vencio, R.
View original source ↗Export metadata ↓

Abstract

Recent advances, in high-throughput technologies allows whole transcriptome analysis, providing a complete and panoramic view of intragenic differential expression in eukaryotes. However, intragenic differential expression in prokaryotes still mystery and incompletely understood. In this study, we investigated and collected the evidence for intragenic differential expression in several archaeal transcriptomes such as, Halobacterium salinarum NRC-1, Pyrococcus furiosus, Methanococcus maripaludis, and Sulfolobus solfataricus, based on computational methods; specifically, by well-known self-organizing map (SOM) for cluster analysis, which transforms high dimensional data into low dimensional. We found 104 (3.86%) of genes in Halobacterium salinarum NRC-1, 59 (2.56%) of genes in Pyrococcus furiosus, 43 (2.41%) of genes Methanococcus maripaludis and 13 (0.42%) of genes in Sulfolobus solfataricus have two or more clusters, i.e., showed the intragenic differential expression at different conditions.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Khan, A., Z. N. Vencio, R.. 2017-07-14. Intragenic differential expression in archaea transcriptomes revealed by computational analysis of tiling microarrays. https://doi.org/10.1101/163436

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

VIGAT: VLM-guided gene activity quantification from single-cell ATAC-seq

Summary: Gene activity quantification from single-cell ATAC-seq is challenging because genomic regions informative of cell identity can vary across genes and tissues, while broadly accessible regions may provide limited cell-type-discriminative information. We developed VIGAT, a vision-language model (VLM)-guided tool that automates visual interpretation of ATAC coverage plots for adaptive gene activity quantification. VIGAT jointly examines accessibility patterns across multiple marker genes, integrating genomic annotations and biological context to select gene-specific regions enriched for cluster-discriminative signals. Selected regions are used to generate a gene-by-cell activity matrix. Across 196 paired single-cell RNA-seq and ATAC-seq datasets, VIGAT outperformed five existing methods in recovering marker-associated accessibility enriched in the corresponding cell populations, demonstrating the utility of VLM-guided visual reasoning for scalable gene activity quantification. Availability and implementation: VIGAT is implemented as an open-source R package and is freely available at https://github.com/beijie02/VIGAT.

bioinformatics↗
bioRxiv2026

Improving real-time search through deep learning-enhanced fragment ion indexing

The fragment ion indexed database searching approach enables highly efficient tandem mass spectrometry database searches, including real-time search workflows. However, the index can require tens of gigabytes of memory, often exceeding the free memory available on instrument acquisition computers running the search. This restricts the size of the searchable database, the variable modification space, and the number of searches that can run concurrently. To address this problem, we developed an index-pruning pipeline within the Comet database search tool using Carafe, a deep learning tool that predicts fragment ion intensities based on peptide sequences. In our approach, only fragment ion positions that are predicted to be observable are written in the index, eliminating entries that are unlikely to contribute to spectrum detection. We evaluated this approach on two search spaces in three index configurations to demonstrate that masking significantly reduced the number of fragment ion index entries and, consequently, peak search memory use. Masked searches were also faster than unmasked searches and identified a comparable number of peptide-spectrum matches. Because all prediction and masking steps are performed before searching, their cost is amortized across repeated, future searches of the same database and modification configuration, introducing no per-spectrum search overhead. The complete implementation is open source with an Apache license.

bioinformatics↗
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↗