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Ge, S. X.

Publications and source records attributed to Ge, S. X..

2 recordsLinked to original sources

From Orphan Genes to Cryptogenic Gene Candidates: Reassessing Uniqueness

BackgroundOrphan genes lack recognizable homologues outside a given taxonomic unit; thus, they have elusive evolutionary origins. They have long been invoked to explain lineage specific traits in medicine and evolutionary biology. Here, we re-analyzed a 2023 repository of putative orphan genes compiled from over 80,000 bacterial species. Using iterative homology-based analyses, we reassessed each genes taxonomic distribution across a broader genomic landscape. ResultsThese results challenge the long-held assertion that species truly harbor large numbers of orphan genes and instead demonstrate that their prevalence has been overestimated. Many "orphan genes" from 2023 now match homologues in other bacterial taxa (2025), demonstrating that sparse sampling had inflated earlier orphan counts. To better reflect these findings, we propose the term Cryptogenic Gene Candidates (CGCs). This offers a more precise alternative to orphan genes, as it more accurately reflects the provisional and potentially temporary nature of these genes apparent uniqueness. This revised terminology acknowledges that many genes identified as having a unique origin may lose this status as more genomes are sequenced. Although our analyses substantially reduced the number of false-positive CGCs, it cannot determine whether a given CGC truly encodes a functional protein or is an artifact of bioinformatic analysis. To minimize annotation artifacts, we then applied additional computational filters to prioritize candidates most likely to encode bona fide proteins. ConclusionsThis work revises the understanding of orphan gene prevalence, establishes CGCs as a more accurate classification, and provides a prioritized set of candidates to guide future experimental studies.

bioinformatics↗

iDEP: An integrated web application for differential expression and pathway analysis

The analysis and interpretation of the RNA-Seq data can be time-consuming and challenging. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 208 annotation databases for plant and animal species, and 2 web services. The workflow can be reproduced by downloading customized R code and related files. As demonstrated by two examples, iDEP (http://ge-lab.org/idep/) democratizes access to bioinformatics resources and empowers biologists to easily gain actionable insights from transcriptomic data.

bioinformatics↗