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1. A long-read and short-read transcriptomics approach provides the first high-quality reference transcriptome and genome annotation for Pseudotsuga menziesii (Douglas-fir)

   https://doi.org/10.1093/g3journal/jkac304  

   Velasco, Vera Marjorie; Ferreira, Alyssa; Zaman, Sumaira; Noordermeer, Devin; Ensminger, Ingo; Wegrzyn, Jill L (December 2022, G3 Genes|Genomes|Genetics)

   Holland, J. (Ed.)

   Abstract Douglas-fir (Pseudotsuga menziesii) is native to western North America. It grows in a wide range of environmental conditions and is an important timber tree. Although there are several studies on the gene expression responses of Douglas-fir to abiotic cues, the absence of high-quality transcriptome and genome data is a barrier to further investigation. Like for most conifers, the available transcriptome and genome reference dataset for Douglas-fir remains fragmented and requires refinement. We aimed to generate a highly accurate, and complete reference transcriptome and genome annotation. We deep-sequenced the transcriptome of Douglas-fir needles from seedlings that were grown under nonstress control conditions or a combination of heat and drought stress conditions using long-read (LR) and short-read (SR) sequencing platforms. We used 2 computational approaches, namely de novo and genome-guided LR transcriptome assembly. Using the LR de novo assembly, we identified 1.3X more high-quality transcripts, 1.85X more “complete” genes, and 2.7X more functionally annotated genes compared to the genome-guided assembly approach. We predicted 666 long noncoding RNAs and 12,778 unique protein-coding transcripts including 2,016 putative transcription factors. We leveraged the LR de novo assembled transcriptome with paired-end SR and a published single-end SR transcriptome to generate an improved genome annotation. This was conducted with BRAKER2 and refined based on functional annotation, repetitive content, and transcriptome alignment. This high-quality genome annotation has 51,419 unique gene models derived from 322,631 initial predictions. Overall, our informatics approach provides a new reference Douglas-fir transcriptome assembly and genome annotation with considerably improved completeness and functional annotation. 

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2. On de novo Bridging Paired-end RNA-seq Data

   https://doi.org/10.1145/3584371.3612987  

   Li, Xiang; Shao, Mingfu (September 2023, Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics)

   The high-throughput short-reads RNA-seq protocols often produce paired-end reads, with the middle portion of the fragments being unsequenced. We explore if the full-length fragments can be com- putationally reconstructed from the sequenced two ends in the absence of the reference genome—a problem here we refer to as de novo bridging. Solving this problem provides longer, more infor- mative RNA-seq reads, and benefits downstream RNA-seq analysis such as transcript assembly, expression quantification, and splic- ing differential analysis. However, de novo bridging is a challeng- ing and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data pro- vides sufficient information for accurate bridging, let alone efficient algorithms that determine the true bridges. Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying com- pacted de Bruijn graph (cdBG) used in the latter task often contains millions of vertices and edges. We designed a new truncated Dijk- stra’s algorithm for this problem, and proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Di- jkstra’s algorithm from scratch for all vertices for further speeding up. These innovative techniques result in scalable algorithms that can bridge all paired-end reads in a cdBG with millions of vertices. Our experiments showed that paired-end RNA-seq reads can be accurately bridged to a large extent. The resulting tool is freely available at https://github.com/Shao-Group/rnabridge-denovo. 

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3. Sequencing Strategy to Ensure Accurate Plasmid Assembly

   https://doi.org/10.1021/acssynbio.4c00539  

   Hernandez, Sarah I; Berezin, Casey-Tyler; Miller, Katie M; Peccoud, Samuel J; Peccoud, Jean (November 2024, ACS Synthetic Biology)

   Despite the wide use of plasmids in research and clinical production, the need to verify plasmid sequences is a bottleneck that is too often underestimated in the manufacturing process. Although sequencing platforms continue to improve, the method and assembly pipeline chosen still influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, especially for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies had issues resolving GC-rich regions, and long-read assemblies commonly had small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate, consistent assembly generation and identified mutations relative to the reference sequence. Although Sanger sequencing can be used to verify specific regions, some GC-rich and repetitive regions were difficult to resolve using any method, suggesting that easily sequenced genetic parts should be prioritized in the design of new genetic constructs. 

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4. Evaluating Illumina-, Nanopore-, and PacBio-based genome assembly strategies with the bald notothen, Trematomus borchgrevinki

   https://doi.org/10.1093/g3journal/jkac192  

   Rayamajhi, Niraj; Cheng, Chi-Hing Christina; Catchen, Julian M (July 2022, G3 Genes|Genomes|Genetics)

   Abstract For any genome-based research, a robust genome assembly is required. De novo assembly strategies have evolved with changes in DNA sequencing technologies and have been through at least three phases: i) short-read only, ii) short- and long-read hybrid, and iii) long-read only assemblies. Each of the phases has their own error model. We hypothesized that hidden scaffolding errors in short-read assembly and erroneous long-read contigs degrades the quality of short- and long-read hybrid assemblies. We assembled the genome of T. borchgrevinki from data generated during each of the three phases and assessed the quality problems we encountered. We developed strategies such as k-mer-assembled region replacement, parameter optimization, and long-read sampling to address the error models. We demonstrated that a k-mer based strategy improved short-read assemblies as measured by BUSCO while mate-pair libraries introduced hidden scaffolding errors and perturbed BUSCO scores. Further, we found that although hybrid assemblies can generate higher contiguity they tend to suffer from lower quality. In addition, we found long-read only assemblies can be optimized for contiguity by sub-sampling length-restricted raw reads. Our results indicate that long-read contig assembly is the current best choice and that assemblies from phase I and phase II were of lower quality. 

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5. Pincho: A Modular Approach to High Quality De Novo Transcriptomics

   https://doi.org/10.3390/genes12070953  

   Ortiz, Randy; Gera, Priyanka; Rivera, Christopher; Santos, Juan C. (July 2021, Genes)

   Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console. 

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