Search bioRxiv⌕ Search

bioRxiv · 10.1101/216077

TraRECo: A Greedy Approach based de novo Transcriptome Assembler with Read Error Correction using Consensus Matrix

Abstract

BackgroundChallenges in developing a good de novo transcriptome assembler include how to deal with read errors and sequence repeats. Almost all de novo assemblers utilize de Bruijn graph, which has a complexity linearly growing with data size while suffers from errors and repeat. Although one can correct errors by inspecting topological structure of the graph, it is an uneasy task when there are too many branches. There are two research directions: improving either graph reliability or path search precision. We focused on improving the reliability.\n\nResultsWe present TraRECo, a greedy approach to de novo assembly employing error-aware graph construction. The idea is similar to overlap-layout-consensus approach used for genome assembly, but is different in that consensus is made through the entire graph construction step. Basically, we built contigs by direct read alignment within a distance margin and performed junction search to construct splicing graphs. While doing so, however, a contig of length l was represented by 4x1 matrix (called consensus matrix), of which each element was the base count of aligned reads so far. A representative sequence is obtained, by taking majority in each column of the consensus matrix, to be used for further read alignment. Once splicing graphs were obtained, we used IsoLasso to find paths with noticeable read depth. The experiments using real and simulated reads showed that the method provides considerable improvements in sensitivity and reasonably better performances when comparing both sensitivity and precision. This could be achieved by making more erroneous reads to be participated in graph construction, which, in turn, improved the depth information quality used for the subsequent path search step. The results for simulated reads showed also challenges are still remaining since non-negligible percentage of transcripts with high abundance were not recovered by the assemblers we considered.\n\nConclusionde novo assembly is mainly to explore not-yet-discovered isoforms and must be able to represent as much reads as possible in an efficient way. In this sense, TraRECo provides us a potential alternative to improve graph reliability, even though the computational burden can be much higher than single k-mer de Bruijn graph approach.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yoon, S., Kim, D., Kang, K., Park, W. J.. 2017-11-08. TraRECo: A Greedy Approach based de novo Transcriptome Assembler with Read Error Correction using Consensus Matrix. https://doi.org/10.1101/216077

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

KEEP EXPLORING

Related preprints

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↗

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↗

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↗