MaxSSmap: a GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

ColoSeq Provides Comprehensive Lynch and Polyposis Syndrome Mutational Analysis Using Massively Parallel Sequencing

In current practice, Next Generation Sequencing (NGS) applications start with mapping/aligning short reads to the reference genome, with the aim of identifying genetic variants. Although existing alignment tools have shown great accuracy in mapping short reads to the reference genome, a significant number of short reads still remain unmapped and are often excluded from downstream analyses thereby causing nonnegligible information loss in the subsequent variant calling procedure. This paper describes Genesis-indel, a computational pipeline that explores the unmapped reads to identify novel indels that are initially missed in the original procedure. Genesis-indel is applied to the unmapped reads of 30 breast cancer patients from TCGA. Results show that the unmapped reads are conserved between the two subtypes of breast cancer investigated in this study and might contribute to the divergence between the subtypes. Genesis-indel identifies 72,997 novel high-quality indels previously not found, among which 16,141 have not been annotated in the widely used mutation database. Statistical analysis of these indels shows significant enrichment of indels residing in oncogenes and tumour suppressor genes. Functional annotation further reveals that these indels are strongly correlated with pathways of cancer and can have high to moderate impact on protein functions. Additionally, some of the indels overlap with the genes that do not have any indel mutations called from the originally mapped reads but have been shown to contribute to the tumorigenesis in multiple carcinomas, further emphasizing the importance of rescuing indels hidden in the unmapped reads in cancer and disease studies.

Next generation sequencing (NGS) technologies have enabled cheap, large-scale, and high-throughput production of short DNA sequence reads and thereby have promoted the explosive growth of data volume. Unfortunately, the produced reads are short and prone to contain errors that are incurred during sequencing cycles. Both large data volume and sequencing errors have complicated the mapping of NGS reads onto the reference genome and have motivated the development of various aligners for very short reads, typically less than 100 base pairs (bps) in length. As read length continues to increase, propelled by advances in NGS technologies, these longer reads tend to have higher sequencing error rates and more true mutations (including substitutions, insertions, or deletions) to the genome. Such new characteristics make inefficient the aligners, which are optimized for very short reads and support only ungapped alignments or gapped alignments with very limited number of gaps (typically one gap), and thereby call for new aligners with fully gapped alignment supported. In this chapter, we present the CUSHAW software suite for NGS read alignment, which is open-source and consists of three individual aligners: CUSHAW, CUSHAW2, and CUSHAW3. This suite offers parallel and efficient NGS read alignments to large genomes, such as the human genome, by harnessing multi-core CPUs or compute unified device architecture (CUDA)-enabled graphics processing units (GPUs). Moreover, it has the capability to align both base-space and color-space reads and is consistently shown to be one of the best alignment tools through our performance evaluations.

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. 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. The ReMILO software can be downloaded for free under Artistic License 2.0 from this site: https://github.com/songc001/remilo. baoe@bjtu.edu.cn. Supplementary data are available at Bioinformatics online.

The introduction of next-generation sequencing techniques and especially the high-throughput systems Solexa (Illumina Inc.) and SOLiD (ABI) made the mapping of short reads to reference sequences a standard application in modern bioinformatics. Short-read alignment is needed for reference based re-sequencing of complete genomes as well as for gene expression analysis based on transcriptome sequencing. Several approaches were developed during the last years allowing for a fast alignment of short sequences to a given template. Methods available to date use heuristic techniques to gain a speedup of the alignments, thereby missing possible alignment positions. Furthermore, most approaches return only one best hit for every query sequence, thus losing the potentially valuable information of alternative alignment positions with identical scores. We developed SARUMAN (Semiglobal Alignment of short Reads Using CUDA and NeedleMAN-Wunsch), a mapping approach that returns all possible alignment positions of a read in a reference sequence under a given error threshold, together with one optimal alignment for each of these positions. Alignments are computed in parallel on graphics hardware, facilitating an considerable speedup of this normally time-consuming step. Combining our filter algorithm with CUDA-accelerated alignments, we were able to align reads to microbial genomes in time comparable or even faster than all published approaches, while still providing an exact, complete and optimal result. At the same time, SARUMAN runs on every standard Linux PC with a CUDA-compatible graphics accelerator. http://www.cebitec.uni-bielefeld.de/brf/saruman/saruman.html.

MotivationBased on the next generation genome sequencing technologies, a variety of biological applications are developed, while alignment is the first step once the sequencing reads are obtained. In recent years, many software tools have been developed to efficiently and accurately align short reads to the reference genome. However, there are still many reads that can't be mapped to the reference genome, due to the exceeding of allowable mismatches. Moreover, besides the unmapped reads, the reads with low mapping qualities are also excluded from the downstream analysis, such as variance calling. If we can take advantages of the confident segments of these reads, not only can the alignment rates be improved, but also more information will be provided for the downstream analysis.ResultsThis paper proposes a method, called RAUR (Re-align the Unmapped Reads), to re-align the reads that can not be mapped by alignment tools. Firstly, it takes advantages of the base quality scores (reported by the sequencer) to figure out the most confident and informative segments of the unmapped reads by controlling the number of possible mismatches in the alignment. Then, combined with an alignment tool, RAUR re-align these segments of the reads. We run RAUR on both simulated data and real data with different read lengths. The results show that many reads which fail to be aligned by the most popular alignment tools (BWA and Bowtie2) can be correctly re-aligned by RAUR, with a similar Precision. Even compared with the BWA-MEM and the local mode of Bowtie2, which perform local alignment for long reads to improve the alignment rate, RAUR also shows advantages on the Alignment rate and Precision in some cases. Therefore, the trimming strategy used in RAUR is useful to improve the Alignment rate of alignment tools for the next-generation genome sequencing.AvailabilityAll source code are available at http://netlab.csu.edu.cn/bioinformatics/RAUR.html.

Metagenomics enables direct investigation of the gene content and potential functions of gut bacteria without isolation and culture. However, metagenome-assembled genomes are often incomplete and have low contiguity due to challenges in assembling repeated genomic elements. Long-read sequencing approaches have successfully yielded circular bacterial genomes directly from metagenomes, but these approaches require high DNA input and can have high error rates. Illumina has recently launched the Illumina Complete Long Read (ICLR) assay, a new approach for generating kilobase-scale reads with low DNA input requirements and high accuracy. Here, we evaluate the performance of ICLR sequencing for gut metagenomics for the first time. We sequenced a microbial mock community and 10 human gut microbiome samples with standard, shotgun 2 × 150 paired-end sequencing, ICLR sequencing, and nanopore long-read sequencing and compared performance in read lengths, assembly contiguity, and bin quality. We find that ICLR human metagenomic assemblies have higher N50 (119.5 ± 24.8 kilobases) than short read assemblies (9.9 ± 4.5 kilobases; P = 0.002), and comparable N50 to nanopore assemblies (91.0 ± 43.8 kilobases; P = 0.32). Additionally, we find that ICLR draft microbial genomes are more complete (94.0% ± 20.6%) than nanopore draft genomes (85.9% ± 23.0%; P ≤ 0.001), and that nanopore draft genomes have truncated gene lengths (924.6 ± 114.7 base pairs) relative to ICLR genomes (954.6 ± 71.5 base pairs; P ≤ 0.001). Overall, we find that ICLR sequencing is a promising method for the accurate assembly of microbial genomes from gut metagenomes.IMPORTANCEMetagenomic sequencing allows scientists to directly measure the genome content and structure of microbes residing in complex microbial communities. Traditional short-read metagenomic sequencing methods often yield fragmented genomes, whereas advanced long-read sequencing methods improve genome assembly quality but often suffer from high error rates and are logistically limited due to high input requirements. A new method, the Illumina Complete Long Read (ICLR) assay, is capable of generating highly accurate kilobase-scale sequencing reads with minimal input material. To evaluate the utility of ICLR in metagenomic contexts, we applied short-read, long-read, and ICLR methods to simple and complex microbial communities. We found that ICLR outperforms short-read methods and yields comparable metagenomic assemblies to standard long-read approaches while requiring less input material. Overall, ICLR represents an additional option for assembling complete genomes from complex metagenomes.

Repetitive sequences and genomic variations mediated by repetitive sequences remain a barrier to our understanding of the function and dynamics of the human genome. Only recently have high throughput short and long reads sequencing provided an opportunity to overcome these challenges. In this thesis, I investigate the distribution, evolution and polymorphism of two types of repeats: subtelomeric and inverted repeats. I also investigate two distinct aspects of genomic mutations related to these repetitive sequences: inversion polymorphisms, and structural variation in chromosome subtelomeric regions.In the first chapter, I provide an overview of our current knowledge, as well as gaps in our understanding of the structure of the human genome. I also provide an overview of the sequencing technologies I will use in this thesis.In the second chapter, I show the performance of inversion detection by short read sequencing. Next, I develop and apply an approach for using long read nanopore sequencing data to detect inversion polymorphism. I show that using long reads it is possible to detect inversions mediated by long inverted repeats which are invisible to paired-end short read sequencing.In the third chapter, I investigate the distribution of inverted repeats in the human genome. I combined this information with existing maps of inversion polymorphisms to investigate the role of inverted repeats in mediating formation of inversions.In the fourth chapter, I apply BioNano data to anchor and extend chromosome ends. This analysis uncovers a surprising level of polymorphism in human chromosome end extension, suggesting an ongoing process of terminal loss and extension. I confirm these chromosome end extension using both nanopore and pacbio long read sequence data.In the fifth chapter, I investigate the evolution of chromosome termini. I identify ancient chromosome terminal ends by investigation of subtelomeric interstitial telomere sequences across multiple mammalian species. This analysis provides an estimate of the rate at which chromosome ends have extended.

To the Editor: PhymmBL1 is a classification system designed for metagenomics experiments that assigns taxonomic labels to short DNA reads. Since the introduction of PhymmBL in 2009, we made extensive changes and added new features, of which we outline the most important ones here (Supplementary Table 1). We also describe results indicating that PhymmBL effectively classifies samples containing mixed eukaryotic and prokaryotic DNA. PhymmBL combines two components: (i) composition-directed taxonomic predictions from Phymm and (ii) basic local alignment search tool (BLAST)-based homology results2. PhymmBL combines these to label each input sequence with its best guess as to the taxonomy of the source organism. Input sequences as short as 100 base pairs can be phylogenetically classified with PhymmBL more accurately than with any other existing method1 including recently introduced methods (Supplementary Note 1). PhymmBL predicts species, genus, family, order, class and phylum for each read, allowing users to arrange results according to levels of specificity relevant to their research goals. We describe how to configure and operate PhymmBL in a parallelized or grid environment in Supplementary Note 2. PhymmBL’s open-source software runs on all UNIX-like systems, is written in Perl and C++ and can be downloaded free of charge (http://www.cbcb.umd.edu/software/phymmbl/; currently version 3.2). To demonstrate PhymmBL’s ability to classify eukaryotic DNA, we classified 2,278,901 short reads (average, 276 base pairs) from a permafrost-preserved woolly mammoth bone metagenome3. Before classification, we added a variety of genomes to PhymmBL’s local database, representing plants, multicellular animals and protests (Supplementary Table 2). We also built models for the elephant (Loxodonta africana) genome (Elephant Genome Project), expecting that woolly mammoth reads would be labeled as elephant. Our goal was to examine whether PhymmBL could identify eukaryotic DNA as accurately as it had identified bacterial DNA. The most abundant label (59.7%) was indeed elephant (Fig. 1). The next three most abundant predictions were Flavobacterium johnsoniae (2.4%), Polaromonas naphthalenivorans (1.6%) and Polaromonas sp. JS666 (1.0%), all three of which are known Arctic bacteria. These likely represent modern bacterial species present on the mammoth bone. PhymmBL therefore effectively separated eukaryotic from prokaryotic reads and accurately predicted the closest relative of the particular eukaryote that was sequenced, despite the presence of other potentially competing eukaryotic genomes in the local database. Details of the computational resources used by PhymmBL are available in Supplementary Note 3. Figure 1 Screenshot of predicted genus-level taxonomic distribution for the mammoth metagenome, focused on genus Loxodonta. PhymmBL output now includes scores reflecting the software’s confidence that its predictions are correct. A confidence score between 0 and 1 appears with each clade-level prediction for each input read. Polynomial functions estimate accuracy, mapping read length and raw score to these normalized confidence scores. For notes on use and algorithmic details of the confidence score computation, see Supplementary Note 4. Users now can add an arbitrary amount of custom genomic data (as single genomes or in batches) to their PhymmBL database, eliminating exclusive reliance on RefSeq bacterial and archaeal genomes. The default classification database can be augmented with private (or even synthetic) genomes or with additional publicly available genomic data. Eukaryotic and viral sequences can also be added, expanding PhymmBL’s classification mandate beyond prokaryotes. For viewing PhymmBL output (Fig. 1), the freely available Krona package (http://krona.sourceforge.net/) provides an interactive viewer (B. Ondov, N. Bergman and A. Phillippy; personal communication). Krona offers an intuitive HTML5 interface that helps users explore predicted taxonomies for metagenomic read sets. Interacting with a dynamic radial visualization (Fig. 1) that changes to highlight designated areas of interest, users can view predictions for their sample population at different levels of phylogenetic granularity, with clade metadata and population statistics displayed alongside.

Fusion transcripts play an important role in a variety of human cancers. But bioinformatics algorithms that use RNA-Seq data to detect fusions tend to yield high false-positive rates due to both relatively short reads from next-generation sequencing (NGS) and the repetitive elements in human genome. The primary purpose of this chapter is to discuss the design strategy of the bioinformatics methods used for detection of fusion transcripts and to compare their strengths and weaknesses, or “fit for purpose,” on RNA-Seq data from non-fixed or formalin-fixed paraffin-embedded (FFPE) tumor tissues. A large number of archival FFPE tumor tissue samples are associated with mature medical records including disease outcome; these samples offer great potential for diagnostic and therapeutic target discovery. However, the chemical treatment by formalin causes RNA degradation and base deamination, which lead to low library complexity and mapping quality. It is important that bioinformatics tools are designed to address these challenges. Here we illustrate a framework to address them, using gFuse as the example. We present results by comparing the fusion transcripts discovered from analysis of RNA from fresh and FFPE MCF-7 breast cancer cells. We also describe the application of gFuse to RNA-Seq data generated from two independent breast cancer cohorts with clinical outcomes and identify candidate fusion transcripts relevant to disease progression.

Somatic mutations are particularly relevant for long-lived organisms. Sources of somatic mutations include imperfect DNA repair, replication errors, and exogenous damage such as ultraviolet radiation. A previous study estimated a surprisingly low number of somatic mutations in a 234-year-old individual of the pedunculate oak (Quercus robur), known as the Napoleon Oak. It has been suggested that the true number of somatic mutations was underestimated due to gaps in the reference genome and too conservative filtering of potential mutations. We therefore generated new high-fidelity long-read data for the Napoleon Oak (n = 12) to produce both a pseudo-haploid genome assembly and a partially phased diploid assembly. The high heterozygosity allowed for complete reconstruction of phased and gapless centromeres for 22 of the 24 chromosomes. On the other hand, the high heterozygosity posed challenges for short-read alignments. Use of only the pseudo-haploid assembly as a reference led to potential misalignments, while use of only the diploid assembly reduced variant detection sensitivity. Since most somatic mutations are layer-specific, the fraction of reads covering a specific somatic mutation is expected to be relatively low, even where all cells in a single layer contain a specific mutation. To address this challenge, we employed a read assignment strategy, selecting the appropriate reference sequence (pseudo-haploid or diploid) based on alignment score and mapping quality. Ultimately, we identified 198 high-confidence somatic mutations, compared with 17 somatic mutations identified before with the same set of short reads. Our approach thus increased the total estimated annual mutation rate by a factor of 5.

In order to overcome the problems of low temperature monitoring accuracy and high control error rate existing in traditional cutting temperature control methods, a new adaptive temperature control method for CNC machine tools is proposed in this paper. In order to improve the adaptive control accuracy of cutting temperature of CNC machine tools, a fuzzy monitor of cutting temperature is designed to realize the real-time monitoring of cutting temperature of CNC machine tools. Based on the real-time monitoring results of cutting temperature of CNC machine tools, Gauss heat source model is used to describe the heat flow distribution of cutting heat source. In two-dimensional space, through the heat source density and boundary conditions, the cutting temperature adaptive control function is constructed to complete the cutting temperature adaptive function of CNC machine tool. The experimental results show that, compared with the traditional control method, this method can accurately monitor the cutting temperature of CNC machine tools, and can accurately carry out the adaptive control of cutting temperature, and the maximum control error rate is less than 3%.

As an important branch of medical robot, rehabilitaction training robot for hemiplegic upper limbs is a hot research topic. Based on motor relearning program, it covers many technology fields, such as rehabilitation medicine, human anatomy, mechanics, computer science, and robotics, etc. With the developing of auxiliary robot, there are still some disadvantages in the existent devices, such as heavy weight, few degrees of freedom, non-portable, low accuracy. These defects make it difficult for the rehabilitation robot to be applied to the actual rehabilitation practice. In this paper, we designed a rehabilitation robot with three DOFs. Compared to the previous device, this design can overcome some shortcomings which makes it flexible, portable, light weight, high accuracy and easy to wear. In this paper, the general structure of the flexible robot manipulator is introduced. The experiment is designed to test and analyse the error in its movement. The application of flexible transmission can greatly reduce the weight of the patient's arm, but may lead to errors in accuracy. Therefore, it is necessary to design experiments to test and analyse errors. The results show that the device can be easily and smartly dressed and has high accuracy.

The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous or both type of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based CPD-GPA (RT), prior CPD-GPA (P), mixture CPD-GPA (M), and prior mixture CPD-GPA (P+M). The method RT performs the peak alignment based only on the retention time distance, while the methods P, M, and P+M carry out the peak alignment using both the retention time distance and mass spectral similarity. The method P incorporates the mass spectral similarity through prior information and the methods M and P+M use the mixture distance measure. Four developed algorithms are applied to homogeneous and heterogeneous spiked-in data as well as two real biological data and compared with three existing algorithms, mSPA, SWPA, and BiPACE-2D. The results show that our CPD-GPA algorithms perform better than all existing algorithms in terms of F1 score.