BACKGROUND: Viruses can inhabit their hosts in the form of an ensemble of various mutant strains. Reconstructing a robust consensus representation for these diverse mutant strains is essential for recognizing the genetic variations among strains and delving into aspects like virulence, pathogenesis, and selecting therapies. Virus genomes are typically small, often composed of only a few thousand to several hundred thousand nucleotides. While constructing a high-quality consensus of virus strains might seem feasible, most current assemblers only generated fragmented contigs. It's important to emphasize the significance of assembling a single full-length consensus contig, as it's vital for identifying genetic diversity and estimating strain abundance accurately. RESULTS: In this paper, we developed FC-Virus, a de novo genome assembly strategy specifically targeting highly diverse viral populations. FC-Virus first identifies the k-mers that are common across most viral strains, and then uses these k-mers as a backbone to build a full-length consensus sequence covering the entire genome. We benchmark FC-Virus against state-of-the-art genome assemblers. CONCLUSION: Experimental results confirm that FC-Virus can construct a single, accurate full-length consensus, whereas other assemblers only manage to produce fragmented contigs. FC-Virus is freely available at https://github.com/qdu-bioinfo/FC-Virus.git .

MOTIVATION: The identification of cancer subtypes plays a crucial role in cancer research and treatment. With the rapid development of high-throughput sequencing technologies, there has been an exponential accumulation of cancer multi-omics data. Integrating multi-omics data has emerged as a cost-effective and efficient strategy for cancer subtyping. While current methods primarily rely on genomics data, protein expression data offers a closer representation of phenotype. Therefore, integrating protein expression data holds promise for enhancing subtyping accuracy. However, the scarcity of protein expression data compared to genomics data presents a challenge in its direct incorporation into existing methods. Moreover, striking a balance between omics-specific learning and cross-omics learning remains a prevalent challenge in current multi-omics integration methods. RESULTS: We introduce Subtype-MGTP, a novel cancer subtyping framework based on the translation of Multiple Genomics To Proteomics. Subtype-MGTP comprises two modules: a translation module, which leverages available protein data to translate multi-type genomics data into predicted protein expression data, and an improved deep subspace clustering module, which integrates contrastive learning to cluster the predicted protein data, yielding refined subtyping results. Extensive experiments conducted on benchmark datasets demonstrate that Subtype-MGTP outperforms nine state-of-the-art cancer subtyping methods. The interpretability of clustering results is further supported by the clinical and survival analysis. Subtype-MGTP also exhibits strong robustness against varying rates of missing protein data and demonstrates distinct advantages in integrating multi-omics data with imbalanced multi-omics data. AVAILABILITY AND IMPLEMENTATION: The code and results are available at https://github.com/kybinn/Subtype-MGTP.

Contigs assembled from the third-generation sequencing long reads are usually more complete than the second-generation short reads. However, the current algorithms still have difficulty in assembling the long reads into the ideal complete and accurate genome, or the theoretical best result [1]. To improve the long read contigs and with more and more fully sequenced genomes available, it could still be possible to use the similar genome-assisted reassembly method [2], which was initially proposed for the short reads making use of a closely related genome (similar genome) to the sequencing genome (target genome). The method aligns the contigs and reads to the similar genome, and then extends and refines the aligned contigs with the aligned reads. Here, we introduce AlignGraph2, a similar genome-assisted reassembly pipeline for the PacBio long reads. The AlignGraph2 pipeline is the second version of AlignGraph algorithm proposed by us but completely redesigned, can be inputted with either error-prone or HiFi long reads, and contains four novel algorithms: similarity-aware alignment algorithm and alignment filtration algorithm for alignment of the long reads and preassembled contigs to the similar genome, and reassembly algorithm and weight-adjusted consensus algorithm for extension and refinement of the preassembled contigs. In our performance tests on both error-prone and HiFi long reads, AlignGraph2 can align 5.7-27.2% more long reads and 7.3-56.0% more bases than some current alignment algorithm and is more efficient or comparable to the others. For contigs assembled with various de novo algorithms and aligned to similar genomes (aligned contigs), AlignGraph2 can extend 8.7-94.7% of them (extendable contigs), and obtain contigs of 7.0-249.6% larger N50 value and 5.2-87.7% smaller number of indels per 100 kbp (extended contigs). With genomes of decreased similarities, AlignGraph2 also has relatively stable performance. The AlignGraph2 software can be downloaded for free from this site: https://github.com/huangs001/AlignGraph2.

MOTIVATION: The third generation PacBio long reads have greatly facilitated sequencing projects with very large read lengths, but they contain about 15% sequencing errors and need error correction. For the projects with long reads only, it is challenging to make correction with fast speed, and also challenging to correct a sufficient amount of read bases, i.e. to achieve high-throughput self-correction. MECAT is currently among the fastest self-correction algorithms, but its throughput is relatively small (Xiao et al., 2017). RESULTS: Here, we introduce FLAS, a wrapper algorithm of MECAT, to achieve high-throughput long-read self-correction while keeping MECAT's fast speed. FLAS finds additional alignments from MECAT prealigned long reads to improve the correction throughput, and removes misalignments for accuracy. In addition, FLAS also uses the corrected long-read regions to correct the uncorrected ones to further improve the throughput. In our performance tests on Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana and human long reads, FLAS can achieve 22.0-50.6% larger throughput than MECAT. FLAS is 2-13× faster compared to the self-correction algorithms other than MECAT, and its throughput is also 9.8-281.8% larger. The FLAS corrected long reads can be assembled into contigs of 13.1-29.8% larger N50 sizes than MECAT. AVAILABILITY AND IMPLEMENTATION: The FLAS software can be downloaded for free from this site: https://github.com/baoe/flas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Contigs assembled from the second generation sequencing short reads may contain misassemblies, and thus complicate downstream analysis or even lead to incorrect analysis results. Fortunately, with more and more sequenced species available, it becomes possible to use the reference genome of a closely related species to detect misassemblies. In addition, long reads of the third generation sequencing technology have been more and more widely used, and can also help detect misassemblies. Results: Here, we introduce ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies. In our performance test on short read assemblies of human chromosome 14 data, ReMILO can detect 41.8-77.9% extensive misassemblies and 33.6-54.5% local misassemblies. On hybrid short and long read assemblies of S.pastorianus data, ReMILO can also detect 60.6-70.9% extensive misassemblies and 28.6-54.0% local misassemblies. Availability and implementation: The ReMILO software can be downloaded for free under Artistic License 2.0 from this site: https://github.com/songc001/remilo. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: The third generation PacBio SMRT long reads can effectively address the read length issue of the second generation sequencing technology, but contain approximately 15% sequencing errors. Several error correction algorithms have been designed to efficiently reduce the error rate to 1%, but they discard large amounts of uncorrected bases and thus lead to low throughput. This loss of bases could limit the completeness of downstream assemblies and the accuracy of analysis. RESULTS: Here, we introduce HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region's repeats in the contigs sufficiently similar to it (similar repeat based alignment approach). It then constructs a contig graph and, for each long read, references the other long reads' alignments to find the most accurate alignment and correct it with the aligned contig regions (long read support based validation approach). Even though some long read regions without the true genome regions in the contigs are corrected with their repeats, this approach makes it possible to further refine these long read regions with the initial insufficient short reads and correct the uncorrected regions in between. In our performance tests on E. coli, A. thaliana and Maylandia zebra data sets, HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms. CONCLUSIONS: The HALC software can be downloaded for free from this site: https://github.com/lanl001/halc .

Resilience is the property that enables a system to continue operating properly when one or more faults occur. Nowadays, as software systems become more and more complex, their hardware execution platforms also become more heterogenous with larger scale. Software systems may fail due to some faults such as node breakdown, communication failure, or data processing failure. In this paper, we propose a ring-based resilience mechanism, which implements fault detection and recovery. (1) To solve the problem that the central server may have high burden of network traffic, we design a ring-based heartbeat algorithm for crash fault detection. (2) We also design a light-weight recovery mechanism to recover from crash faults as compared with the current system-specific mechanisms. To evaluate our mechanism, we use a 3D-online game system as a case study. By injecting faults, we test the effectiveness and overhead of the proposed mechanism. Compared with other mechanisms, the experimental results show that our mechanism can support resilience very well and is better at dealing with the crash fault caused by high cluster workload with acceptable overhead.

Improving image resolution has broad applications and is an important research topic. Recently, a hybrid method Adaptive Sparse Domain Selection (ASDS) combining a reconstruction‐based method and an example‐based method has been proposed to take advantage of the two, but may not reconstruct sufficient details. In this study, the authors propose to improve ASDS: Zeyde's method is first used to obtain an intermediate image with high‐frequency details, and then the obtained image is used to replace the autoregressive model of ASDS as the example‐based term. In addition, the authors may split the input image into patches and use different parameter settings for the patches of different amount of details. Experimental results demonstrate the improved hybrid methods can produce high‐quality images quantitatively and perceptually.

MOTIVATION: De novo assemblies of genomes remain one of the most challenging applications in next-generation sequencing. Usually, their results are incomplete and fragmented into hundreds of contigs. Repeats in genomes and sequencing errors are the main reasons for these complications. With the rapidly growing number of sequenced genomes, it is now feasible to improve assemblies by guiding them with genomes from related species. RESULTS: Here we introduce AlignGraph, an algorithm for extending and joining de novo-assembled contigs or scaffolds guided by closely related reference genomes. It aligns paired-end (PE) reads and preassembled contigs or scaffolds to a close reference. From the obtained alignments, it builds a novel data structure, called the PE multipositional de Bruijn graph. The incorporated positional information from the alignments and PE reads allows us to extend the initial assemblies, while avoiding incorrect extensions and early terminations. In our performance tests, AlignGraph was able to substantially improve the contigs and scaffolds from several assemblers. For instance, 28.7-62.3% of the contigs of Arabidopsis thaliana and human could be extended, resulting in improvements of common assembly metrics, such as an increase of the N50 of the extendable contigs by 89.9-94.5% and 80.3-165.8%, respectively. In another test, AlignGraph was able to improve the assembly of a published genome (Arabidopsis strain Landsberg) by increasing the N50 of its extendable scaffolds by 86.6%. These results demonstrate AlignGraph's efficiency in improving genome assemblies by taking advantage of closely related references. AVAILABILITY AND IMPLEMENTATION: The AlignGraph software can be downloaded for free from this site: https://github.com/baoe/AlignGraph.

MOTIVATION: De novo transcriptome assemblies of RNA-Seq data are important for genomics applications of unsequenced organisms. Owing to the complexity and often incomplete representation of transcripts in sequencing libraries, the assembly of high-quality transcriptomes can be challenging. However, with the rapidly growing number of sequenced genomes, it is now feasible to improve RNA-Seq assemblies by guiding them with genomic sequences. RESULTS: This study introduces BRANCH, an algorithm designed for improving de novo transcriptome assemblies by using genomic information that can be partial or complete genome sequences from the same or a related organism. Its input includes assembled RNA reads (transfrags), genomic sequences (e.g. contigs) and the RNA reads themselves. It uses a customized version of BLAT to align the transfrags and RNA reads to the genomic sequences. After identifying exons from the alignments, it defines a directed acyclic graph and maps the transfrags to paths on the graph. It then joins and extends the transfrags by applying an algorithm that solves a combinatorial optimization problem, called the Minimum weight Minimum Path Cover with given Paths. In performance tests on real data from Caenorhabditis elegans and Saccharomyces cerevisiae, assisted by genomic contigs from the same species, BRANCH improved the sensitivity and precision of transfrags generated by Velvet/Oases or Trinity by 5.1-56.7% and 0.3-10.5%, respectively. These improvements added 3.8-74.1% complete transcripts and 8.3-3.8% proteins to the initial assembly. Similar improvements were achieved when guiding the BRANCH processing of a transcriptome assembly from a more complex organism (mouse) with genomic sequences from a related species (rat). AVAILABILITY: The BRANCH software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/branch. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Similarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads. RESULTS: Here, we introduce SEED-an efficient algorithm for clustering very large NGS sets. It joins sequences into clusters that can differ by up to three mismatches and three overhanging residues from their virtual center. It is based on a modified spaced seed method, called block spaced seeds. Its clustering component operates on the hash tables by first identifying virtual center sequences and then finding all their neighboring sequences that meet the similarity parameters. SEED can cluster 100 million short read sequences in <4 h with a linear time and memory performance. When using SEED as a preprocessing tool on genome/transcriptome assembly data, it was able to reduce the time and memory requirements of the Velvet/Oasis assembler for the datasets used in this study by 60-85% and 21-41%, respectively. In addition, the assemblies contained longer contigs than non-preprocessed data as indicated by 12-27% larger N50 values. Compared with other clustering tools, SEED showed the best performance in generating clusters of NGS data similar to true cluster results with a 2- to 10-fold better time performance. While most of SEED's utilities fall into the preprocessing area of NGS data, our tests also demonstrate its efficiency as stand-alone tool for discovering clusters of small RNA sequences in NGS data from unsequenced organisms. AVAILABILITY: The SEED software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/seed. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.