Grabfast: A CUDA based GPU accelerated fast short sequence alignment algorithm

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.

During 2012, next generation sequencing (NGS) has attracted great attention in the biomedical research community, especially for personalized medicine. Also, third generation sequencing has become available. Therefore, state-of-art sequencing technology and analysis are reviewed in this Bioinformatics spotlight on 2012. Next-generation sequencing (NGS) is high-throughput nucleic acid sequencing technology with wide dynamic range and single base resolution. The full promise of NGS depends on the optimization of NGS platforms, sequence alignment and assembly algorithms, data analytics, novel algorithms for integrating NGS data with existing genomic, proteomic, or metabolomic data, and quantitative assessment of NGS technology in comparing to more established technologies such as microarrays. NGS technology has been predicated to become a cornerstone of personalized medicine. It is argued that NGS is a promising field for motivated young researchers who are looking for opportunities in bioinformatics.

In medical and genomic research, the Next Generation Sequencing (NGS) has a major role. Presently, NGS data are produced at the rate of 10 TB a day and challenge the storage and data processing capacities. These huge datasets are being used by a wide sort of applications such as customized cancer healing and precision medicine. NGS technologies offer prospects for understanding unidentified species and complex syndrome. To utilize genomic data for such applications, the genomic data in the form of short reads produced by NGS initially has to be assembled into whole genome sequence. And then, the sequences have to be compared for similarity and variation discovery which will be useful for analyzing and arriving at health-related solutions. In this paper, the fundamental methods for short read alignment such as assembly-based and alignment-based methods are discussed. Followed by which, the different ways to compare the sequences to check the alignment for similarity/dissimilarity discovery are discussed. This comparative analysis report can be utilized for health-related medical decisions.

Motivation and Objectives The output of a high throughput next generation sequencing (NGS) machine is a collection of short reads, which have to be properly assembled in order to reconstruct the original DNA sequence of the analyzed organism (Metzker, 2010; Earl, 2011). The DNA sequence assembly process is based on aligning and merging these reads for effectively reconstructing the real primary structure of the DNA sample sequence or reference genome. The use of NGS machines results in much larger sets of reads to be assembled, posing new problems for computer scientists and bioinformaticians. In particular, a relevant issue is related with the trade-off between precision of the assembly process and its computational time, stating the need for faster methods that can keep pace with the speed and volume of reads that are generated with NGS. An important step in DNA assembly is the identification of a subset of read pairs that have a high probability of being aligned sequentially in the reconstruction. Such step is often referred to as filtering, and amounts in selecting a significantly smaller subset of the initial set of read pairs (whose dimension is quadratic in the number of initial reads) that can be then processed by an alignment algorithm, usually quite time consuming. The desired effect of filtering is then to quickly filter out from the candidate set of read pairs those that would not provide a good alignment in the following phase. The computation cost of filtering should then be balanced by the speed-up obtained when a smaller set of read pairs is considered for alignment. In this work we propose and test the use of alignment free distances to evaluate the similarity between two short reads as a technique for filtering good read pairs to be assembled. The method operates in constant time in the string length and is tested in its ability to emulate, with a proper level of precision, much more time consuming methods to evaluate the similarity between short DNA sequences, such as the established Needleman-Wunsch edit distance (Needleman, Wunsch, and Christian, 1970), often used in the final step of the assembly procedure. These preliminary experiments show the efficacy of this approach for filtering the promising read pairs - eligible candidates to successfully assemble the entire genome of a given organism. Therefore, the alignment free reads filtering may significantly accelerate the assembly process without a substantial loss in accuracy of the DNA sample sequence reconstruction. Methods ODNA sequence assembly The DNA sequence assembly process is based on the alignment and merging of reads (stretch of sequences) in order to reconstruct the original primary structure of the DNA sample sequences. Given a set of sequences S = {s1, s2, …, sn}, where s ∈ S is a fragment of the primary structure of DNA (read) (e.g. s = {ATTCGA...CTGACT}), assembly is in charge of building the longest sequence from the set S where each pair of consequent reads obey certain similarity conditions. DNA read pairs filtering and Alignment Free Distance This step identifies the promising read pairs in order to reduce the amount of input data given to the real assembly algorithm. We adopted a very quick measure of the similarity between two reads, Alignment Free (AF) based distance (Vinga and Almeida, 2003). AF computes the similarity of two strings based only on the dictionary of their substrings, irrespective of their relative position. As a dictionary we considered the set of 4-mer (sequences composed of 4 different nucleotides) and then built a profile for each read composed by the relative frequencies of each 4-mer in the read. The Euclidean distance between the profiles of two reads was taken as an inverse measure of the similarity of the two reads and thus as an indication that the two reads formed a promising pair to be considered in the assembly phase. AF filtering was then used defining a proper threshold on the AF distance and discarding all the pairs that exhibited a AF distance above the threshold. Computational complexity of AF distance is a constant linearly bounded by the number of k-mers adopted and the length of the strings to be compared. Comparing with other distances: Needleman-Wunsch and “Bowtie” distance Along with AF we considered the well-established Needleman-Wunsch edit distance (NW) and compared them in their ability to identify significant pairs. This comparison was based on the computation of a sort of perfect distance computed after an alignment over an already known sequence has been performed. Such distance, referred to as Bowtie distance (BT), was obtained as follows: a. a large number of reads coming from a known sequence were considered; b. hese reads were aligned over the known sequence using the standard Bowtie algorithm (Langmead et al, 2009); c. any two reads received a maximum BT distance if their alignment did not intersect over the reference sequence, else they received a distance inversely proportional to their intersection over the sequence (e.g., they would have BT distance equal to 0 if they were aligned one on top (or inside) of the other by the Bowtie algorithm). By construction we assumed BT distance to be the reference distance, e.g., the distance that expressed the best possible alignments - being based on the knowledge of the reference sequence - and tested the correlation of AF and NW with BT; moreover, we verified the ability of AF and NW to predict that a given read pair had BT distance above or below a given threshold. Results and Discussion For our test we considered the E.Coli genome and a set of reads from this genome reads obtained by Roche 454 sequencing machine. Reads have average length of ~235 nucleotides and standard deviation of approx. 10 (the large majority of them having length in the interval 225-245). Reads were aligned with the reference sequence with Bowtie and then 100,000 were sampled at random according to their alignment along the sequence. Reads were considered both forward and reversed, giving rise to a total of 200,0002 read pairs. All 620,798 read pairs with BT distance < 1 were considered for the experiments; then, out of the remaining pairs, 233,099 were sampled at random. A total of 853,897 read pairs composed the working data set. For all these reads, NW distance and AF distance over the 4-mer were computed. AF, NW and BT distances were all normalized between 0 (maximal similarity) and 1 (maximal dissimilarity). The first interesting results was that the correlation between distances showed that AF approximates BT somehow better than NW: we obtained a correlation coefficient of 0.761 for AF and BT, compared with a smaller 0.706 when NW and BT were considered (coherently, correlation between AF and NW is 0,721). The second interesting results was obtained when we compared the ability of AF and NW to predict whether BT was above or below a given threshold. We defined a threshold predictor for a given function F2 based on function F1 and on a given pair a1, a2 as follows: if (F1 < a1) then predict (F2 < a2), else predict (F2 ≥ a2). To a given pair (a1, a2), we associated the measure of True Positive rate (TP) (percentage of cases where (F1 < a1) and (F2 < a2) and of True Negative rate (TN) (percentage of cases where (F1 ≥ a1) and (F2 ≥ a2); analogously we defined False Positive rate (FP) and False Negative rate (FN). For each (a1, a2) with both values ranging from 0 to 1, we then computed, with step 0.05, the positive and negative error rates taking AF as a predictor of BT and NW as a predictor of BT. Part of the results are summarized in the charts of Figure 1, that show for 3 different levels of a2 (0.1, 0.2, and 0.3) the precision of the predictors (y-axis) when the value of a1 is changed (x-axis), both when AF is used as a predictor of BT (charts in the first row) and when NW is used as a predictor of BT (charts in second row). Similar results are obtained also for other levels of a2, here omitted for brevity. The curves bring to light very clearly how AF is a very good threshold predictor for BT for the considered data; despite its light computational complexity, it appears to perform significantly better than the more complex NW edit distance when its ability to support a threshold predictor is considered. Acknowledgements The authors are partially supported by the FLAGSHIP ”InterOmics” project (PB.P05) funded by the Italian MIUR and CNR institutions, and by the cooperative programme 2010–2012 between the National Research Council of Italy (CNR) and the Polish Academy of Sciences (PAN). References Earl D et al (2011); Assemblathon 1: A competitive assessment of de novo short read assembly methods; Genome Research, 21 Langmead B, Trapnell C, et al (2009); Ultrafast and memory-efficient alignment of short DNA sequences to the human genome; Genome Biology, 10:R25 Metzker ML (2010); Sequencing technologies — the next generation; Nat Rev Genet., 11(1) Needleman SB, Wunsch CD. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3): 443–53 Vinga S, Almeida J (2003); Alignment-free sequence comparison—a review, Bioinformatics 19 (4), 513-523 Note: Figures and tables are available in PDF version only.

In this paper, we investigate the feasibility of computing DNA patterns on FPGAs by utilising high-performance and parallel computing. In particular, the report analyses and contrasts the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the dynamic and heuristic alternatives that are currently on the market. In this research project, we investigate the use of parallel and high-performance computing to determine how DNA patterns can be computed on FPGAs. In particular, the report details the findings of a study that compared the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the algorithm's current dynamic and heuristic programming methods. The study was carried out by comparing these three aspects of the algorithm. The traditional methods of sequence alignment that are software-implied are not adequate to meet the actual data rate requirements. A strategy that is based on the use of hardware makes it possible to process multiple new databases in parallel, which enables a high degree of scalability. A finite state machine serves as the primary data processor in our demonstration of how to organize the sequences of amino acids in a protein (FSM). We also investigate the effectiveness of bit-based sequence alignment algorithms and present the inner stage pipelined FPGA architecture for implementing sequence alignment. Both of these are presented in this paper. Synchronized controllers are utilized in the programming techniques of the algorithm for the purpose of parallel sequence alignment. Inference is the foundation of traditional software-based methods for sequence alignment, but these methods are unable to keep up with the required data rates. A strategy that is based on hardware allows for the processing of parallel tasks with a large number of new databases and provides excellent scalability. Our explanation of the classification of protein sequences is based on a Finite State Machine, which serves as the primary processing unit (FSM). In addition to this, we investigate how well bit-based sequence alignment algorithms work and present the inner stage pipelined Field Programmable Gate Array architecture that can be used to implement sequence alignment. Synchronized controllers are utilized in the process of performing sequence alignment in parallel. The architecture as a whole was made to be scalable and fast, with a small number of computations and the ability to perform parallel processing in hardware using field-programmable gate arrays to compare bits in patterns. Finally, we use FPGA hardware synthesis to show off impressive performance and low resource consumption.

Next-generation sequencing (NGS) technologies have rapidly evolved in the last 5 years, leading to the generation of millions of short reads in a single run. Consequently, various sequence alignment algorithms have been developed to compare these reads to an appropriate reference in order to perform important downstream analysis. SOAP2 from the SOAP series is one of the most commonly used alignment programs to handle NGS data, and it efficiently does so using low computer memory usage and fast alignment speed. This chapter describes the protocol used to align short reads to a reference genome using SOAP2, and highlights the significance of using the in-built command-line options to tune the behavior of the algorithm according to the inputs and the desired results.

Next-generation sequencing (NGS) or high-throughput sequencing platforms refers to different modern sequencing technologies that allow scientist to sequence millions to billions of reads (base pairs) in a single, much more quicker and cheaper cost than the previously used Sanger (first generation) sequencing method. Next-generation sequencing platforms classified as second generation sequencing platforms which require polymerase chain reaction (PCR) amplification and third generation sequencing platforms which do not require PCR amplification for sequence. The appearance of next generation sequencing technologies has revolutionized the genome sequencing of organisms including farm animals. These platforms enable scientists to access latest and detailed information about genetic markers that are responsible for economically important traits. Identification of candidate genes responsible for these traits in different species can bring down the overall cost of livestock breeding by improving productivity and disease resistance. Even though the output and error rate of third generation sequencing platforms (Pacific Biosystems and Nanopore) remain to be improved, they offered long read length relatively with cheaper sequencing cost and easy sample preparation. Oxfored NanoporeMinION devices are the most portable third generation DNA sequencer available in the size of a small cell phone that can be plugged into the USB of a laptop.   Key words: Oxford nanopore, single nucleotide polymorphisms, third-generation.

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.

A Novel PD-L1-targeting Antagonistic DNA Aptamer With Antitumor Effects.

IntroductionWomen with advanced or recurrent endometrial carcinoma (EC) exhibiting HER2 overexpression or gene amplification are candidates for HER2-targeted therapy. Determining HER2 status involves immunohistochemistry (IHC) and, in equivocal cases, fluorescence...

After the reference genomes of many organisms are sequenced in this post-genetic era, it has become an extremely important issue that how to do the re-sequencing and assembly for individual genomes from very large amount of reads. In this paper, we will present a re-sequencing tool designed for the Next Generation Sequencing (NGS) data. And these data are composed of a huge amount of short reads which will be aligned onto a reference genome. We modified and implemented the algorithm of Burrows-Wheeler Transform and FM-index to build the genome index of human, and proposed an idea to segment each short read into multiple non-overlapping seeds, which let us align short reads with large Hamming distance. Finally, we used the simulated datasets and real datasets from 1000 Genome Project to demonstrate the performance of our tool on a personal computer, and compared the results with widely used tools, bowtie and SOAPv2.

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.

In the past five years, next generation sequencing (NGS) technology platforms have been released and have become the tool of choice for sequencing genomes. Methods for sequence finishing have had to be updated to deal with the issues created by NGS platforms. Finishing begins with the assembling of the reads produced by the sequencing machines before computational gap closure is attempted. Remaining gaps are identified and suitable experimental techniques such as polymerase chain reactions are used to close these. Until recently, the aim for many genome sequencing projects has been to fully finish genomes. However, due to the time and cost associated with finishing, the majority of future genomes will only reach a high‐quality draft. New genome standards have had to be defined to better reflect the types of sequencing now being undertaken. Improvements to the current generation of sequencing machines along with the advent of the third‐generation technologies means that new ways of dealing with the data volume will need to be sought. Key Concepts: Next generation sequencing platforms have generated new issues for sequence finishing, which have required novel solutions. Genome standards have had to be updated to better reflect NGS data.

Mapping sequenced reads to a reference genome, also known as sequence reads alignment, is central for sequence analysis. Emerging sequencing technologies such as next generation sequencing (NGS) lead to an explosion of sequencing data, which is far beyond the process capabilities of existing alignment tools. Consequently, sequence alignment becomes the bottleneck of sequence analysis. Intensive computing power is required to address this challenge. A key feature of sequence alignment is that different reads are independent. Considering this property, we proposed a multi-level parallelization strategy to speed up BWA, a widely used sequence alignment tool and developed our massively parallel sequence aligner: mBWA. mBWA contains two levels of parallelization: firstly, parallelization of data input/output (IO) and reads alignment by a three-stage parallel pipeline; secondly, parallelization enabled by Intel Many Integrated Core (MIC) coprocessor technology. In this paper, we demonstrate that mBWA outperforms BWA by a combination of those techniques. To the best of our knowledge, mBWA is the first sequence alignment tool to run on Intel MIC and it can achieve more than 5-fold speedup over the original BWA while maintaining the alignment precision.

ESA: An efficient sequence alignment algorithm for biological database search on Sunway TaihuLight