bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.06.10.144063

RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.

Salifu, S. P. P.·Nyarko, H. N.·Doughan, A.·Msatsi, H. K.·Mensah, I.·Bukari, A.-R. A.
View original source ↗Export metadata ↓

Abstract

The introduction of several differential gene expression analysis tools has made it difficult for researchers to settle on a particular tool for RNA-seq analysis. This coupled with the appropriate determination of biological replicates to give an optimum representation of the study population and make biological sense. To address these challenges, we performed a survey of 8 tools used for differential expression in RNA-seq analysis. We simulated 39 different datasets (from 10 to 200 replicates, at an interval of 5) using compcodeR with a maximum of 100 replicates. Our goal was to determine the effect of varying the number of replicates on the performance (F1-score, recall and precision) of the tools. EBSeq and edgeR-glmRT recorded the highest (0.9385) and lowest (0.6505) average F1-score across all replicates, respectively. We also performed a pairwise comparison of all the tools to determine their concordance with each other in identifying differentially expressed genes. We found the greatest concordance to be between limma voom treat and limma voom ebayes. Finally, we recommend employing edgeR-glmRT for RNA-seq experiments involving 10-50 replicates and edgeR-glmQLF for studies with 55 to 200 replicates. Author summaryDownstream analysis of RNA-seq data in R often poses several challenges to researchers as it is a daunting task to choose a specific differential expression analysis tool over another. Researchers also find it challenging to determine the number (replicates) of samples to use in order to give comparable and accurate results. In this paper, we surveyed eight differential expression analysis tools using different number of replicates of simulated RNA-seq count data. We measured the performance of each tool and based on the recorded F1-scores, recall and precision, we made the following recommendations; consider edgeR-glmRT and edgeR-glmQLF for replicates of 10-50 and 55-200 respectively.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Salifu, S. P. P., Nyarko, H. N., Doughan, A., Msatsi, H. K., Mensah, I., Bukari, A.-R. A.. 2020-06-10. RNA-seq analyses: Benchmarking differential expression analyses tools reveals the effect of higher number of replicates on performance.. https://doi.org/10.1101/2020.06.10.144063

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

KEEP EXPLORING

Related preprints

bioRxiv2026

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗
bioRxiv2026

SIFT: Syncmer-Informed Selection of Haplotypes for T2T Imputation

Genome patching improves the completeness of fragmented assemblies by filling unresolved gaps with sequences from reference genomes. Pangenome panels provide diverse candidate haplotypes for this task, but identifying suitable references efficiently becomes increasingly challenging as these panels grow. We present SIFT, a compressed indexing method for selecting reference haplotypes for genome patching with ImpuT2T. SIFT combines closed-syncmer parsing, phrase-level indexing, and pseudo-maximal exact match (pseudo-MEM) queries. Closed syncmers provide a context-independent parsing rule with bounded distances between consecutive selected positions, enabling consistent parsing of reference and query sequences. We demonstrate that two closed syncmers determine the distance at which they can occur consecutively in any string, so that equal substrings of two parses represent identical sequences and no gap sequence need be stored. SIFT indexes the reference parse and its reverse using run-length compressed suffix arrays and adapts the Boyer-Moore-Li search procedure to identify pseudo-MEMs between the reference parse and query parse. It then aggregates matching evidence to identify candidate reference haplotypes for downstream patching. This framework connects compressed sequence indexing with haplotype selection and provides a basis for querying large reference panels at the phrase level. SIFT is publicly available under the GNU license at https://github.com/EddieFerro/SIFT.

bioinformatics↗
bioRxiv2026

Assessing the Capacity of Genomic Language Models to Generate Informative Embeddings of Viral Genome Fragments

Genomic language models (gLMs) trained on large volumes of nucleotide sequences are becoming an alternative to classical methods in metagenomic analysis, particularly for studying genomic "dark matter". In this work, we investigate the ability of four gLMs (DNABERT-2, DNABERT-S, ViraLM, and Evo2-7B) to generate taxonomically informative vector representations of short viral genome fragments. We constructed a balanced reference dataset of viruses based on the ICTV Virus Metadata Resource and compared the representations generated by gLMs with conventional k-mer profiles and MinHash sketches using clustering and neighborhood-based metrics (adjusted Rand index (ARI), precision@k, and CROSS precision@k). On the reference dataset, three of the four gLMs outperformed the 4-mer profile in terms of ARI and precision@5 under the same evaluation protocol. The best performance was generally achieved by intermediate model layers. Conventional k-mer profiles consistently outperformed MinHash sketches. We further evaluated the methods on two viral orders (egoviruses and mriyaviruses) absent from the reference dataset, with model layers and k-mer lengths selected exclusively on the reference dataset. DNABERT-S showed the most consistently strong performance across these two orders, ranking first on mriyaviruses and second on egoviruses, while k-mer profiles with Jensen-Shannon distance remained competitive. We also observed the consistency of representations between forward fragments and their reverse complements. Overall, the best-performing gLMs surpassed conventional baselines, although their advantage depended on the dataset and evaluation metric. The consistently strong performance of DNABERT-S suggests a potential benefit of contrastive fine-tuning and indicates that larger models are not necessarily more effective for generating taxonomically informative representations.

bioinformatics↗