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1. Grouper: graph-based clustering and annotation for improved de novo transcriptome analysis

   https://doi.org/10.1093/bioinformatics/bty378  

   Malik, Laraib ; Almodaresi, Fatemeh ; Patro, Rob ; Birol, ed., Inanc ( May 2018 , Bioinformatics)

   De novo transcriptome analysis using RNA-seq offers a promising means to study gene expression in non-model organisms. Yet, the difficulty of transcriptome assembly means that the contigs provided by the assembler often represent a fractured and incomplete view of the transcriptome, complicating downstream analysis. We introduce Grouper, a new method for clustering contigs from de novo assemblies that are likely to belong to the same transcripts and genes; these groups can subsequently be analyzed more robustly. When provided with access to the genome of a related organism, Grouper can transfer annotations to the de novo assembly, further improving the clustering.

   On de novo assemblies from four different species, we show that Grouper is able to accurately cluster a larger number of contigs than the existing state-of-the-art method. The Grouper pipeline is able to map greater than 10% more reads against the contigs, leading to accurate downstream differential expression analyses. The labeling module, in the presence of a closely related annotated genome, can efficiently transfer annotations to the contigs and use this information to further improve clustering. Overall, Grouper provides a complete and efficient pipeline for processing de novo transcriptomic assemblies.

   The Grouper software is freely available at https://github.com/COMBINE-lab/grouper under the 2-clause BSD license.

   Supplementary data are available at Bioinformatics online.

    

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2. SCGid: a consensus approach to contig filtering and genome prediction from single-cell sequencing libraries of uncultured eukaryotes

   https://doi.org/10.1093/bioinformatics/btz866  

   Amses, Kevin R. ; Davis, William J. ; James, Timothy Y. ; Birol, ed., Inanc ( November 2019 , Bioinformatics)

   Whole-genome sequencing of uncultured eukaryotic genomes is complicated by difficulties in acquiring sufficient amounts of tissue. Single-cell genomics (SCG) by multiple displacement amplification provides a technical workaround, yielding whole-genome libraries which can be assembled de novo. Downsides of multiple displacement amplification include coverage biases and exacerbation of contamination. These factors affect assembly continuity and fidelity, complicating discrimination of genomes from contamination and noise by available tools. Uncultured eukaryotes and their relatives are often underrepresented in large sequence data repositories, further impairing identification and separation.

   We compare the ability of filtering approaches to remove contamination and resolve eukaryotic draft genomes from SCG metagenomes, finding significant variation in outcomes. To address these inconsistencies, we introduce a consensus approach that is codified in the SCGid software package. SCGid parallelly filters assemblies using different approaches, yielding three intermediate drafts from which consensus is drawn. Using genuine and mock SCG metagenomes, we show that our approach corrects for variation among draft genomes predicted by individual approaches and outperforms them in recapitulating published drafts in a fast and repeatable way, providing a useful alternative to available methods and manual curation.

   The SCGid package is implemented in python and R. Source code is available at http://www.github.com/amsesk/SCGid under the GNU GPL 3.0 license.

   Supplementary data are available at Bioinformatics online.

    

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3. Annotated genome and transcriptome of the endangered Caribbean mountainous star coral (Orbicella faveolata) using PacBio long-read sequencing

   https://doi.org/10.1186/s12864-024-10092-w  

   Young, Benjamin_D ; Williamson, Olivia_M ; Kron, Nicholas_S ; Andrade_Rodriguez, Natalia ; Isma, Lys_M ; MacKnight, Nicholas_J ; Muller, Erinn_M ; Rosales, Stephanie_M ; Sirotzke, Stephanie_M ; Traylor-Knowles, Nikki ; et al ( February 2024 , BMC Genomics)

   Long-read sequencing is revolutionizingde-novogenome assemblies, with continued advancements making it more readily available for previously understudied, non-model organisms. Stony corals are one such example, with long-readde-novogenome assemblies now starting to be publicly available, opening the door for a wide array of ‘omics-based research. Here we present a newde-novogenome assembly for the endangered Caribbean star coral,Orbicella faveolata, using PacBio circular consensus reads. Our genome assembly improved the contiguity (51 versus 1,933 contigs) and complete and single copy BUSCO orthologs (93.6% versus 85.3%, database metazoa_odb10), compared to the currently available reference genome generated using short-read methodologies. Our newde-novoassembled genome also showed comparable quality metrics to other coral long-read genomes. Telomeric repeat analysis identified putative chromosomes in our scaffolded assembly, with these repeats at either one, or both ends, of scaffolded contigs. We identified 32,172 protein coding genes in our assembly through use of long-read RNA sequencing (ISO-seq) of additionalO. faveolatafragments exposed to a range of abiotic and biotic treatments, and publicly available short-read RNA-seq data. With anthropogenic influences heavily affectingO. faveolata, as well as itsincreasing incorporation into reef restoration activities, this updated genome resource can be used for population genomics and other ‘omics analyses to aid in the conservation of this species.

    

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4. Virseqimprover: An Integrated Pipeline for Viral Contig Error Correction, Extension, and Annotation

   https://doi.org/10.21203/rs.3.rs-3318217/v1  

   Song, Haoqiu ; Tithi, Saima ; Aylward, Frank ; Jensen, Roderick ; Zhang, Liqing ( September 2023 , Research Square)

   Background:Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated fromde novoassembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference.Methods:We have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality. Virseqimprover first examines whether there is any chimeric sequence based on read coverage, breaks the sequence into segments if there is, then extends the longest segment with uniform coverage, and repeats these procedures until the sequence cannot be extended. Finally, Virseqimprover annotates the gene content of the resulting sequence.Conclusion:Virseqimprover has good performance on correcting and extending viral contigs to their full lengths, hence can be a useful tool to improve the completeness and minimize the assembly errors of viral contigs. Both a web server and a conda package for Virseqimprover are provided to the research community free of charge.

    

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5. A consensus-based ensemble approach to improve transcriptome assembly

   https://doi.org/10.1186/s12859-021-04434-8  

   Voshall, Adam ; Behera, Sairam ; Li, Xiangjun ; Yu, Xiao-Hong ; Kapil, Kushagra ; Deogun, Jitender S. ; Shanklin, John ; Cahoon, Edgar B. ; Moriyama, Etsuko N. ( October 2021 , BMC Bioinformatics)

   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.

   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.

   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/.

    

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