TransBorrow: genome-guided transcriptome assembly by borrowing assemblies from different assemblers

LSTrAP-denovo: Automated Generation of Transcriptome Atlases for Eukaryotic Species Without Genomes

Despite the abundance of species with transcriptomic data, a significant number of species still lack sequenced genomes, making it difficult to study gene function and expression in these organisms. While de novo transcriptome assembly can be used to assemble protein-coding transcripts from RNA-sequencing (RNA-seq) data, the datasets used often only feature samples of arbitrarily selected or similar experimental conditions, which might fail to capture condition-specific transcripts. We developed the Large-Scale Transcriptome Assembly Pipeline for de novo assembled transcripts (LSTrAP-denovo) to automatically generate transcriptome atlases of eukaryotic species. Specifically, given an NCBI TaxID, LSTrAP-denovo can (1) filter undesirable RNA-seq accessions based on read data, (2) select RNA-seq accessions via unsupervised machine learning to construct a sample-balanced dataset for download, (3) assemble transcripts via over-assembly, (4) functionally annotate coding sequences (CDS) from assembled transcripts and (5) generate transcriptome atlases in the form of expression matrices for downstream transcriptomic analyses. LSTrAP-denovo is easy to implement, written in Python, and is freely available at https://github.com/pengkenlim/LSTrAP-denovo/.

Protocol for transcriptome assembly by the TransBorrow algorithm

High-throughput RNA-seq enables comprehensive analysis of the transcriptome for various purposes. However, this technology generally generates massive amounts of sequencing reads with a shorter read length. Consequently, fast, accurate, and flexible tools are needed for assembling raw RNA-seq data into full-length transcripts and quantifying their expression levels. In this protocol, we report TransBorrow, a novel transcriptome assembly software specifically designed for short RNA-seq reads. TransBorrow is employed in conjunction with a splice-aware alignment tool (e.g. Hisat2 and Star) and some other transcriptome assembly tools (e.g. StringTie, Cufflinks, and Scallop). The protocol encompasses all necessary steps, starting from downloading and processing raw sequencing data to assembling the full-length transcripts and quantifying their expressed abundances. The execution time of the protocol may vary depending on the sizes of processed datasets and computational platforms.

Evolution and development of fruits of Erycina pusilla and other orchid species

Fruits play a crucial role in seed dispersal. They open along dehiscence zones. Fruit dehiscence zone formation has been intensively studied in Arabidopsis thaliana. However, little is known about the mechanisms and genes involved in the formation of fruit dehiscence zones in species outside the Brassicaceae. The dehiscence zone of A. thaliana contains a lignified layer, while dehiscence zone tissues of the emerging orchid model Erycina pusilla include a lipid layer. Here we present an analysis of evolution and development of fruit dehiscence zones in orchids. We performed ancestral state reconstructions across the five orchid subfamilies to study the evolution of selected fruit traits and explored dehiscence zone developmental genes using RNA-seq and qPCR. We found that erect dehiscent fruits with non-lignified dehiscence zones and a short ripening period are ancestral characters in orchids. Lignified dehiscence zones in orchid fruits evolved multiple times from non-lignified zones. Furthermore, we carried out gene expression analysis of tissues from different developmental stages of E. pusilla fruits. We found that fruit dehiscence genes from the MADS-box gene family and other important regulators in E. pusilla differed in their expression pattern from their homologs in A. thaliana. This suggests that the current A. thaliana fruit dehiscence model requires adjustment for orchids. Additionally, we discovered that homologs of A. thaliana genes involved in the development of carpel, gynoecium and ovules, and genes involved in lipid biosynthesis were expressed in the fruit valves of E. pusilla, implying that these genes may play a novel role in formation of dehiscence zone tissues in orchids. Future functional analysis of developmental regulators, lipid identification and quantification can shed more light on lipid-layer based dehiscence of orchid fruits.

Flow Decomposition With Subpath Constraints

Flow network decomposition is a natural model for problems where we are given a flow network arising from superimposing a set of weighted paths and would like to recover the underlying data, i.e., decompose the flow into the original paths and their weights. Thus, variations on flow decomposition are often used as subroutines in multiassembly problems such as RNA transcript assembly. In practice, we frequently have access to information beyond flow values in the form of subpaths, and many tools incorporate these heuristically. But despite acknowledging their utility in practice, previous work has not formally addressed the effect of subpath constraints on the accuracy of flow network decomposition approaches. We formalize the flow decomposition with subpath constraints problem, give the first algorithms for it, and study its usefulness for recovering ground truth decompositions. For finding a minimum decomposition, we propose both a heuristic and an FPT algorithm. Experiments on RNA transcript datasets show that for instances with larger solution path sets, the addition of subpath constraints finds 13% more ground truth solutions when minimal decompositions are found exactly, and 30% more ground truth solutions when minimal decompositions are found heuristically.

LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions

Characterization of the subcellular distribution of RNA is essential for understanding the molecular basis of biological processes. Here, the subcellular nanopore direct RNA-sequencing (DRS) of four lung cancer cell lines (A549, H1975, H358, and HCC4006) is performed, coupled with a computational pipeline, Low-abundance Aware Full-length Isoform clusTEr (LAFITE), to comprehensively analyze the full-length cytoplasmic and nuclear transcriptome. Using additional DRS and orthogonal data sets, it is shown that LAFITE outperforms current methods for detecting full-length transcripts, particularly for low-abundance isoforms that are usually overlooked due to poor read coverage. Experimental validation of six novel isoforms exclusively identified by LAFITE further confirms the reliability of this pipeline. By applying LAFITE to subcellular DRS data, the complexity of the nuclear transcriptome is revealed in terms of isoform diversity, 3'-UTR usage, m6A modification patterns, and intron retention. Overall, LAFITE provides enhanced full-length isoform identification and enables a high-resolution view of the RNA landscape at the isoform level.

Improving RNA Assembly via Safety and Completeness in Flow Decompositions

Decomposing a network flow into weighted paths is a problem with numerous applications, ranging from networking, transportation planning, to bioinformatics. In some applications we look for a decomposition that is optimal with respect to some property, such as the number of paths used, robustness to edge deletion, or length of the longest path. However, in many bioinformatic applications, we seek a specific decomposition where the paths correspond to some underlying data that generated the flow. In these cases, no optimization criteria guarantee the identification of the correct decomposition. Therefore, we propose to instead report the safe paths, which are subpaths of at least one path in every flow decomposition. In this work, we give the first local characterization of safe paths for flow decompositions in directed acyclic graphs, leading to a practical algorithm for finding the complete set of safe paths. In addition, we evaluate our algorithm on RNA transcript data sets against a trivial safe algorithm (extended unitigs), the recently proposed safe paths for path covers (TCBB 2021) and the popular heuristic greedy-width. On the one hand, we found that besides maintaining perfect precision, our safe and complete algorithm reports a significantly higher coverage (≈50% more) compared with the other safe algorithms. On the other hand, the greedy-width algorithm although reporting a better coverage, it also reports a significantly lower precision on complex graphs (for genes expressing a large number of transcripts). Overall, our safe and complete algorithm outperforms (by ≈20%) greedy-width on a unified metric (F-score) considering both coverage and precision when the evaluated data set has a significant number of complex graphs. Moreover, it also has a superior time (4-5×) and space performance (1.2-2.2×), resulting in a better and more practical approach for bioinformatic applications of flow decomposition.

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

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.

Tiglon enables accurate transcriptome assembly via integrating mappings of different aligners

Full-length transcript reconstruction has a pivotal role in RNA-seq data analysis. In this research, we present a new genome-guided transcriptome assembly algorithm, namely Tiglon, which integrates multiple alignments of different mapping tools and builds the labeled splice graphs, followed by a label-based dynamic path-searching strategy to reconstruct the transcripts. We evaluate Tiglon on a simulated dataset and 12 real datasets under the Hisat2 and Star mappings. The results indicate that the integrating techniques of Tiglon exhibit great superiority over the state-of-the-art assemblers, including StringTie2 and Scallop, depending on Hisat2 alignments, Star alignments, or the merged alignments of both. Especially, Tiglon is significantly powerful in recovering lowly expressed transcripts.

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Tiglon is designed for integrating multiple alignments to assemble transcripts

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Integrating alignments of different aligners is helpful for transcriptome assembly

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Tiglon proposes a new graph model called the labeled splice graph

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Our experiments demonstrate that Tiglon outperforms the leading assemblers

TahcoRoll: fast genomic signature profiling via thinned automaton and rolling hash

Objectives

Genomic signatures like k-mers have become one of the most prominent approaches to describe genomic data. As a result, myriad real-world applications, such as the construction of de Bruijn graphs in genome assembly, have been benefited by recognizing genomic signatures. In other words, an efficient approach of genomic signature profiling is an essential need for tackling high-throughput sequencing reads. However, most of the existing approaches only recognize fixed-size k-mers while many research studies have shown the importance of considering variable-length k-mers.

Methods

In this paper, we present a novel genomic signature profiling approach, TahcoRoll, by extending the Aho-Corasick algorithm (AC) for the task of profiling variable-length k-mers. We first group nucleotides into two clusters and represent each cluster with a bit. The rolling hash technique is further utilized to encode signatures and read patterns for efficient matching.

Results

In extensive experiments, TahcoRoll significantly outperforms the most state-of-the-art k-mer counters and has the capability of processing reads across different sequencing platforms on a budget desktop computer.

Conclusions

The single-thread version of TahcoRoll is as efficient as the eight-thread version of the state-of-the-art, JellyFish, while the eight-thread TahcoRoll outperforms the eight-thread JellyFish by at least four times.

A consensus-based ensemble approach to improve transcriptome assembly

BACKGROUND

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes.

RESULTS

In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble.

CONCLUSIONS

Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/ .

Non-coding Natural Antisense Transcripts: Analysis and Application

Non-coding natural antisense transcripts (ncNATs) are regulatory RNA sequences that are transcribed in the opposite direction to protein-coding or non-coding transcripts. These transcripts are implicated in a broad variety of biological and pathological processes, including tumorigenesis and oncogenic progression. With this complex field still in its infancy, annotations, expression profiling and functional characterisations of ncNATs are far less comprehensive than those for protein-coding genes, pointing out substantial gaps in the analysis and characterisation of these regulatory transcripts. In this review, we discuss ncNATs from an analysis perspective, in particular regarding the use of high-throughput sequencing strategies, such as RNA-sequencing, and summarize the unique challenges of investigating the antisense transcriptome. Finally, we elaborate on their potential as biomarkers and future targets for treatment, focusing on cancer.

TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective.