More Accurate Transcript Assembly via Parameter Advising

DeBlasio, Dan; Kim, Kwanho; Kingsford, Carl

doi:10.1089/cmb.2019.0286

Cited by 6 publications

(23 citation statements)

References 25 publications

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“…The heuristic constructions used in this framework, such as the way to define the similarity between two functions, the form of the contrastive loss function, and the architecture of the encoding network can be further improved in the future. Additionally, following DeBlasio et al [2020], we use AUC to estimate the performance of transcript assemblers by comparing predicted transcripts with known ones. However, this is not a perfect measure as it may unfairly penalize assemblers that generate novel transcripts, which can be of significant interest.…”

Section: Discussionmentioning

confidence: 99%

Data-driven AI system for learning how to run transcript assemblers

Shen,

Yan,

Kingsford

2024

Preprint

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Transcript assemblers are tools to reconstruct expressed transcripts from RNA-seq data. These tools have a large number of tunable parameters, and accurate transcript assembly requires setting them suitably. Because of the heterogeneity of different RNA-seq samples, a single default setting or a small fixed set of parameter candidates can only support the good performance of transcript assembly on average, but are often suboptimal for many individual samples. Manually tuning parameters for each sample is time consuming and requires specialized experience. Therefore, developing an automated system that can advise good parameter settings for individual samples becomes an important problem. Using Bayesian optimization and contrastive learning, we develop a new automated parameter advising system for transcript assembly that can generate sets of sample-specific good parameter candidates. Our framework achieves efficient sample-specific parameter advising by learning parameter knowledge from a representative set of existing RNA-seq samples and transferring the knowledge to unseen samples. We use Scallop and StringTie, two well-known transcript assemblers, to test our framework on two collections of RNA-seq samples. Results show that our new parameter advising system significantly outperforms the previous advising method in each dataset and each transcript assembler. Scripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/autoparadvisor.

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Section: Discussionmentioning

confidence: 99%

Data-driven AI system for learning how to run transcript assemblers

Shen,

Yan,

Kingsford

2024

Preprint

Self Cite

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“…Then, the hypothesis test probability ( p -value) was calculated by statistical model. Third, multiple hypothesis testing correction (Benjiamini and Hochberg method) ( 14 ) was performed to obtain the corrected p -value (false discovery rate, FDR). The DESeq2 package in R was used to screen genes with significant differences between different samples ( 15 ).…”

Section: Methodsmentioning

confidence: 99%

The alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension, a study with transcriptome sequencing and bioinformatic analysis

Deng

Meng

et al. 2022

Front. Cardiovasc. Med.

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BackgroundAt present, the alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension (CTEPH) remain unclear. We aimed to compare the difference of molecular markers and signaling pathways in patients with CTEPH and healthy people with transcriptome sequencing and bioinformatic analysis.MethodsWe prospectively included 26 patients with CTEPH and 35 sex- and age-matched healthy volunteers as control. We extracted RNA from whole blood samples to construct the library. Then, qualified libraries were sequenced using PE100 strategy on BGIseq platform. Subsequently, the DESeq2 package in R was used to screen differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) of 7 patients with CTEPH and 5 healthy volunteers. Afterwards, we performed functional enrichment and protein–protein interaction analysis of DEmRNAs. We also performed lncRNA-mRNA co-expression analysis and lncRNA-miRNA-mRNA network construction. In addition, we performed diagnostic analysis on the GSE130391 dataset. Finally, we performed reverse transcription polymerase chain reaction (RT-PCR) of genes in 19 patients with CTEPH and 30 healthy volunteers.ResultsGender and age between patients with CTEPH and healthy controls, between sequencing group and in vitro validation group, were comparable. A total of 437 DEmRNAs and 192 DElncRNAs were obtained. Subsequently, 205 pairs of interacting DEmRNAs and 232 pairs of lncRNA-mRNA relationship were obtained. DEmRNAs were significantly enriched in chemokine signaling pathway, metabolic pathways, arachidonic acid metabolism, and MAPK signaling pathway. Only one regulation pathway of SOBP-hsa-miR-320b-LINC00472 was found through ceRNA network construction. In diagnostic analysis, the area under curve (AUC) values of LINC00472, PIK3R6, SCN3A, and TCL6, respectively, were 0.964, 0.893, 0.750, and 0.732.ConclusionThe identification of alterations in molecules and pathways may provide further research directions on pathogenesis of CTEPH. Additionally, LINC00472, PIK3R6, SCN3A, and TCL6 may act as the potential gene markers in CTEPH.

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“…While the transcript assembly problem has attracted great interest from the community, with a proliferation of methods proposed [10], [11], [15], [16], [17], [18], [19], [20], [23], [56], [57], [58], [59], [60], [61], assembling RNA-seq reads remains a challenge, with RNA assembly methods having a precision under 50%-60% on human data [18], [62]. In addition, current algorithms that aim to produce fulllength transcripts employ various heuristics and thresholds to increase contiguity of the transcript under assembly, which makes results vary significantly in quality with different parameter settings [63].…”

Section: Application To Rna Transcript Assemblymentioning

confidence: 99%

Safety in Multi-Assembly via Paths Appearing in All Path Covers of a DAG

Cáceres¹,

Mumey²,

Husić³

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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A multi-assembly problem asks to reconstruct multiple genomic sequences from mixed reads sequenced from all of them. Standard formulations of such problems model a solution as a path cover in a directed acyclic graph, namely a set of paths that together cover all vertices of the graph. Since multi-assembly problems admit multiple solutions in practice, we consider an approach commonly used in standard genome assembly: output only partial solutions (contigs, or safe paths), that appear in all path cover solutions. We study constrained path covers, a restriction on the path cover solution that incorporate practical constraints arising in multi-assembly problems. We give efficient algorithms finding all maximal safe paths for constrained path covers. We compute the safe paths of splicing graphs constructed from transcript annotations of different species. Our algorithms run in less than 15 seconds per species and report RNA contigs that are over 99% precise and are up to 8 times longer than unitigs. Moreover, RNA contigs cover over 70% of the transcripts and their coding sequences in most cases. With their increased length to unitigs, high precision, and fast construction time, maximal safe paths can provide a better base set of sequences for transcript assembly programs.

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More Accurate Transcript Assembly via Parameter Advising

Cited by 6 publications

References 25 publications

Data-driven AI system for learning how to run transcript assemblers

Data-driven AI system for learning how to run transcript assemblers

The alterations in molecular markers and signaling pathways in chronic thromboembolic pulmonary hypertension, a study with transcriptome sequencing and bioinformatic analysis

Safety in Multi-Assembly via Paths Appearing in All Path Covers of a DAG

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