ParAlign: a parallel sequence alignment algorithm for rapid and sensitive database searches

There is a need for faster and more sensitive algorithms for sequence similarity searching in view of the rapidly increasing amounts of genomic sequence data available. Parallel processing capabilities in the form of the single instruction, multiple data (SIMD) technology are now available in common microprocessors and enable a single microprocessor to perform many operations in parallel. The ParAlign algorithm has been specifically designed to take advantage of this technology. The new algorithm initially exploits parallelism to perform a very rapid computation of the exact optimal ungapped alignment score for all diagonals in the alignment matrix. Then, a novel heuristic is employed to compute an approximate score of a gapped alignment by combining the scores of several diagonals. This approximate score is used to select the most interesting database sequences for a subsequent Smith-Waterman alignment, which is also parallelised. The resulting method represents a substantial improvement compared to existing heuristics. The sensitivity and specificity of ParAlign was found to be as good as Smith-Waterman implementations when the same method for computing the statistical significance of the matches was used. In terms of speed, only the significantly less sensitive NCBI BLAST 2 program was found to outperform the new approach. Online searches are available at http://dna.uio.no/search/     

Striped Smith-Waterman speeds database searches six times over other SIMD implementations

MOTIVATION: The only algorithm guaranteed to find the optimal local alignment is the Smith-Waterman. It is also one of the slowest due to the number of computations required for the search. To speed up the algorithm, Single-Instruction Multiple-Data (SIMD) instructions have been used to parallelize the algorithm at the instruction level. RESULTS: A faster implementation of the Smith-Waterman algorithm is presented. This algorithm achieved 2-8 times performance improvement over other SIMD based Smith-Waterman implementations. On a 2.0 GHz Xeon Core 2 Duo processor, speeds of >3.0 billion cell updates/s were achieved. AVAILABILITY: http://farrar.michael.googlepages.com/Smith-waterman     

Speed improvements of peptide-spectrum matching using single-instruction multiple-data instructions

Peptide-spectrum matching is one of the most time-consuming portion of the database search method for assignment of tandem mass spectra to peptides. In this study, we develop a parallel algorithm for peptide-spectrum matching using Single-Instruction Multiple Data (SIMD) instructions. Unlike other parallel algorithms in peptide-spectrum matching, our algorithm parallelizes the computation of matches between a single spectrum and a given peptide sequence from the database. It also significantly reduces the number of comparison operations. Extra improvements are obtained by using SIMD instructions to avoid conditional branches and unnecessary memory access within the algorithm. The implementation of the developed algorithm is based on the Streaming SIMD Extensions technology that is embedded in most Intel microprocessors. Similar technology also exists in other modern microprocessors. A simulation shows that the developed algorithm achieves an 18-fold speedup over the previous version of Real-Time Peptide-Spectrum Matching algorithm [F. X. Wu et al., Rapid Commun. Mass Sepctrom. 2006, 20, 1199-1208]. Therefore, the developed algorithm can be employed to develop real-time control methods for MS/MS.     

Reference master: A microcomputer-based storage and retrieval system for bibliographic references

A complete system for housekeeping and retrieval of bibliographic references managing individual reprint collections is described. By the use of special hardware and individual data base software even large reprint collections in the range up to 65 000 papers are handled economically. A fast 8-bit microprocessor (HD 64180) in combination with a Winchester hard disk drive serves as the basis for rapid access to the desired information. An efficient string search algorithm written in assembly language guarantees a fast operation with a search speed of more than 6,000 entries/ minute. The system cannot only prepare reference lists and reference files, but also incorporates an editor and maintains the control whether reprints are already on file or requested. The implementation of back-up schemes assure against data losses. Using a state of the art design single board computer and the most recent mass storage device technology, the system is as well small and cost effective, and thus suitable for personal use. In addition, some general questions and pitfalls concerning the management of scientific literature collections are touched upon.     

Flexible,Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Motivation

To obtain large-scale sequence alignments in a fast and flexible way is an important step in the analyses of next generation sequencing data. Applications based on the Smith-Waterman (SW) algorithm are often either not fast enough, limited to dedicated tasks or not sufficiently accurate due to statistical issues. Current SW implementations that run on graphics hardware do not report the alignment details necessary for further analysis.

Results

With the Parallel SW Alignment Software (PaSWAS) it is possible (a) to have easy access to the computational power of NVIDIA-based general purpose graphics processing units (GPGPUs) to perform high-speed sequence alignments, and (b) retrieve relevant information such as score, number of gaps and mismatches. The software reports multiple hits per alignment. The added value of the new SW implementation is demonstrated with two test cases: (1) tag recovery in next generation sequence data and (2) isotype assignment within an immunoglobulin 454 sequence data set. Both cases show the usability and versatility of the new parallel Smith-Waterman implementation.     

A table-driven, full-sensitivity similarity search algorithm.

Searching a database for a local alignment to a query under a typical scoring scheme, such as PAM120 or BLOSUM62 with affine gap costs, is a computation that has resisted algorithmic improvement due to its basis in dynamic programming and the weak nature of the signals being searched for. In a query preprocessing step, a set of tables can be built that permit one to (a) eliminate a large fraction of the dynamic programming matrix from consideration and (b) to compute several steps of the remainder with a single table lookup. While this result is not an asymptotic improvement over the original Smith-Waterman algorithm, its complexity is characterized in terms of some sparse features of the matrix and it yields the fastest software implementation to date for such searches.     

GeneRAGE: a robust algorithm for sequence clustering and domain detection

MOTIVATION: Efficient, accurate and automatic clustering of large protein sequence datasets, such as complete proteomes, into families, according to sequence similarity. Detection and correction of false positive and negative relationships with subsequent detection and resolution of multi-domain proteins. RESULTS: A new algorithm for the automatic clustering of protein sequence datasets has been developed. This algorithm represents all similarity relationships within the dataset in a binary matrix. Removal of false positives is achieved through subsequent symmetrification of the matrix using a Smith-Waterman dynamic programming alignment algorithm. Detection of multi-domain protein families and further false positive relationships within the symmetrical matrix is achieved through iterative processing of matrix elements with successive rounds of Smith-Waterman dynamic programming alignments. Recursive single-linkage clustering of the corrected matrix allows efficient and accurate family representation for each protein in the dataset. Initial clusters containing multi-domain families, are split into their constituent clusters using the information obtained by the multi-domain detection step. This algorithm can hence quickly and accurately cluster large protein datasets into families. Problems due to the presence of multi-domain proteins are minimized, allowing more precise clustering information to be obtained automatically. AVAILABILITY: GeneRAGE (version 1.0) executable binaries for most platforms may be obtained from the authors on request. The system is available to academic users free of charge under license.     

SAP-A Sequence Mapping and Analyzing Program for Long Sequence Reads Alignment and Accurate Variants Discovery

The third-generation of sequencing technologies produces sequence reads of 1000 bp or more that may contain high polymorphism information. However, most currently available sequence analysis tools are developed specifically for analyzing short sequence reads. While the traditional Smith-Waterman (SW) algorithm can be used to map long sequence reads, its naive implementation is computationally infeasible. We have developed a new Sequence mapping and Analyzing Program (SAP) that implements a modified version of SW to speed up the alignment process. In benchmarks with simulated and real exon sequencing data and a real E. coli genome sequence data generated by the third-generation sequencing technologies, SAP outperforms currently available tools for mapping short and long sequence reads in both speed and proportion of captured reads. In addition, it achieves high accuracy in detecting SNPs and InDels in the simulated data. SAP is available at https://github.com/davidsun/SAP.     

Comparison of methods for searching protein sequence databases.

We have compared commonly used sequence comparison algorithms, scoring matrices, and gap penalties using a method that identifies statistically significant differences in performance. Search sensitivity with either the Smith-Waterman algorithm or FASTA is significantly improved by using modern scoring matrices, such as BLOSUM45-55, and optimized gap penalties instead of the conventional PAM250 matrix. More dramatic improvement can be obtained by scaling similarity scores by the logarithm of the length of the library sequence (In()-scaling). With the best modern scoring matrix (BLOSUM55 or JO93) and optimal gap penalties (-12 for the first residue in the gap and -2 for additional residues), Smith-Waterman and FASTA performed significantly better than BLASTP. With In()-scaling and optimal scoring matrices (BLOSUM45 or Gonnet92) and gap penalties (-12, -1), the rigorous Smith-Waterman algorithm performs better than either BLASTP and FASTA, although with the Gonnet92 matrix the difference with FASTA was not significant. Ln()-scaling performed better than normalization based on other simple functions of library sequence length. Ln()-scaling also performed better than scores based on normalized variance, but the differences were not statistically significant for the BLOSUM50 and Gonnet92 matrices. Optimal scoring matrices and gap penalties are reported for Smith-Waterman and FASTA, using conventional or In()-scaled similarity scores. Searches with no penalty for gap extension, or no penalty for gap opening, or an infinite penalty for gaps performed significantly worse than the best methods. Differences in performance between FASTA and Smith-Waterman were not significant when partial query sequences were used. However, the best performance with complete query sequences was obtained with the Smith-Waterman algorithm and In()-scaling.     

Patternhunter II: highly sensitive and fast homology search

Extending the single optimized spaced seed of PatternHunter(20) to multiple ones, PatternHunter II simultaneously remedies the lack of sensitivity of Blastn and the lack of speed of Smith-Waterman, for homology search. At Blastn speed, PatternHunter II approaches Smith-Waterman sensitivity, bringing homology search methodology research back to a full circle.     

A new approach to sequence comparison: normalized sequence alignment

The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.     

SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications

Background

The Smith-Waterman algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools for next-generation sequencing data. Though various fast Smith-Waterman implementations are developed, they are either designed as monolithic protein database searching tools, which do not return detailed alignment, or are embedded into other tools. These issues make reusing these efficient Smith-Waterman implementations impractical.

Results

To facilitate easy integration of the fast Single-Instruction-Multiple-Data Smith-Waterman algorithm into third-party software, we wrote a C/C++ library, which extends Farrar’s Striped Smith-Waterman (SSW) to return alignment information in addition to the optimal Smith-Waterman score. In this library we developed a new method to generate the full optimal alignment results and a suboptimal score in linear space at little cost of efficiency. This improvement makes the fast Single-Instruction-Multiple-Data Smith-Waterman become really useful in genomic applications. SSW is available both as a C/C++ software library, as well as a stand-alone alignment tool at: https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library.

Conclusions

The SSW library has been used in the primary read mapping tool MOSAIK, the split-read mapping program SCISSORS, the MEI detector TANGRAM, and the read-overlap graph generation program RZMBLR. The speeds of the mentioned software are improved significantly by replacing their ordinary Smith-Waterman or banded Smith-Waterman module with the SSW Library.     

Increasing the accuracy of the global alignment of amino acid sequences by constructing a set of alignment candidates

The accuracy of the global Smith-Waterman alignments and Pareto-optimal alignments depending on the degree of sequence similarity (percent of coincidence, % id, and the number of remote fragments NGap) has been examined. An algorithm for constructing a set of three to six alignments has been developed of which the accuracy of the best alignment exceeds on the average the accuracy of the best alignment that can be constructed using the Smith-Waterman algorithm. For weakly homologous sequences (% id 15, NGap 20), the increase in the accuracy is on the average about 8%, with the average accuracy of the global Smith-Waterman alignments being about 38% (the accuracy was estimated on model test sets).     

From analysis of protein structural alignments toward a novel approach to align protein sequences

Alignment of protein sequences is a key step in most computational methods for prediction of protein function and homology-based modeling of three-dimensional (3D)-structure. We investigated correspondence between "gold standard" alignments of 3D protein structures and the sequence alignments produced by the Smith-Waterman algorithm, currently the most sensitive method for pair-wise alignment of sequences. The results of this analysis enabled development of a novel method to align a pair of protein sequences. The comparison of the Smith-Waterman and structure alignments focused on their inner structure and especially on the continuous ungapped alignment segments, "islands" between gaps. Approximately one third of the islands in the gold standard alignments have negative or low positive score, and their recognition is below the sensitivity limit of the Smith-Waterman algorithm. From the alignment accuracy perspective, the time spent by the algorithm while working in these unalignable regions is unnecessary. We considered features of the standard similarity scoring function responsible for this phenomenon and suggested an alternative hierarchical algorithm, which explicitly addresses high scoring regions. This algorithm is considerably faster than the Smith-Waterman algorithm, whereas resulting alignments are in average of the same quality with respect to the gold standard. This finding shows that the decrease of alignment accuracy is not necessarily a price for the computational efficiency.     

Coupling SIMD and SIMT Architectures to Boost Performance of a Phylogeny-aware Alignment Kernel

ABSTRACT: BACKGROUND: Aligning short DNA reads to a reference sequence alignment is a prerequisite fordetecting their biological origin and analyzing them in a phylogenetic context. With thePaPaRa tool we introduced a dedicated dynamic programming algorithm forsimultaneously aligning short reads to reference alignments and correspondingevolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles thatcorrespond to the branches of such a reference tree. The algorithm needs to perform animmense number of pairwise alignments. Therefore, we explore vector intrinsics andGPUs to accelerate the PaPaRa alignment kernel. RESULTS: We optimized and parallelized PaPaRa on CPUs and GPUs. Via SSE 4.1 SIMD (SingleInstruction, Multiple Data) intrinsics for x86 SIMD architectures and multi-threading, weobtained a 9-fold acceleration on a single core as well as linear speedups with respect tothe number of cores. The peak CPU performance amounts to 18.1 GCUPS (Giga CellUpdates per Second) using all four physical cores on an Intel i7 2600 CPU running at 3.4GHz. The average CPU performance (averaged over all test runs) is 12.33 GCUPS. Wealso used OpenCL to execute PaPaRa on a GPU SIMT (Single Instruction, MultipleThreads) architecture. A NVIDIA GeForce 560 GPU delivered peak and averageperformance of 22.1 and 18.4 GCUPS respectively. Finally, we combined the SIMD andSIMT implementations into a hybrid CPU-GPU system that achieved an accumulatedpeak performance of 33.8 GCUPS. CONCLUSIONS: This accelerated version of PaPaRa (available at www.exelixis-lab.org/software.html)provides a significant performance improvement that allows for analyzing larger datasetsin less time. We observe that state-of-the-art SIMD and SIMT architectures delivercomparable performance for this dynamic programming kernel when the "competingprogrammer approach" is deployed. Finally, we show that overall performance can besubstantially increased by designing a hybrid CPU-GPU system with appropriate loaddistribution mechanisms.     

Real time microprocessor-based 50 Hz notch filter for ECG

High performance analogue notch filters are difficult to realize in practice. Their real time digital counterparts, when implemented on an inexpensive microprocessor with no additional hardware, also have limitations of their own. To overcome these limitations, we have developed a new type of 50 Hz notch filter with its poles close to the zeros of the transfer function 1 + Z^−N. This new type of digital notch filter can be used for suppression of 50 Hz noise in the ECG. The filter is simple to design and easy to implement on most 8-bit microprocessors. It has a high execution speed, low analogue to digital noise, low recursive noise and good frequency response with no overshoot or ringing. It is capable of suppressing 50 Hz noise by at least 40 db. Its finite bandwidth of 4 Hz causes about 2% attenuation on the QRS peak, which is acceptable for almost all practical applications. One possible drawback is that multiple notches occur at higher frequencies. However, this has hardly any effect on the ECG because of the limited notch bandwidth.     

Implantable cardioverter defibrillator algorithms: status review in terms of computational cost.

In recent decades, implantable cardioverter defibrillators (ICDs) have improved substantially, becoming the treatment of choice for patients at high risk of life-threatening arrhythmias. Nevertheless, inappropriate shock therapy for non-ventricular arrhythmias is still a problem. Extending the ICD battery lifetime demands very low power consumption, which is obtained at very low microprocessor clock frequencies. Currently, some high-performance algorithms remain beyond the computational capabilities of ICDs. Future ICDs with higher computing power will permit the implementation of computationally intensive algorithms, enhancing the discrimination performance and preventing inappropriate shock therapies. An ICD algorithm status review is presented from the point of view of signal processing techniques and their computational costs. Several examples of discrimination algorithms with increasing computational cost are analyzed. Whereas some of them are already used in commercial ICDs, other algorithms cannot be implemented yet in current ICDs. A solution based on dynamic adaptation of microprocessor power consumption to meet algorithm computational requirements is proposed. This solution allows implementation of complex discrimination algorithms in ICDs without significantly increasing the power consumption.     

Increasing the accuracy of global alignment of amino acid sequences by constructing a set of alignment candidates

The accuracy of global Smith-Waterman alignments and Pareto-optimal alignments depending on the degree of sequence similarity (percent of coincidence, %id, and the number of removed fragments NGap) has been examined. An algorithm for constructing a set of three to six alignments has been developed of which the best alignment on the average exceeds in accuracy the best alignment that can be constructed using the Smith-Waterman algorithm. For weakly homologous sequences (%id 15, NGap 20), the increase in accuracy is on the average about 8%, with the average accuracy of the global Smith-Waterman alignments being about 38% (the accuracy was estimated on model test sets).     

Comparative analysis of primary structure of nucleic acids and proteins

The review considers the original works on the primary structure of biopolymers, which were carried out from 1983 to 2003. Most works were supported by the Russian program Human Genome and earlier similar Russian programs. Little-known publications of 1983-1993 and recent unpublished results are described in detail. In the field of genome comparisons, these concern the OWEN hierarchic algorithm aligning syntenic regions of two genome sequences. The resulting global alignment is obtained as an ordered chain of local similarities. Alignment of sequences sized about 10(6) nucleotides takes several minutes. The concept of local similarity conflicts is generalized to multiple comparisons. New algorithms aligning protein sequences are described and compared with the Smith-Waterman algorithm, which is now most accurate. The ANCHOR hierarchic algorithm generates alignments of much the same accuracy and is twice as rapid as the Smith-Waterman one. The STRSWer algorithm takes an account of the secondary structures of proteins under study. With the secondary structures predicted using the PSI-PRED software for pairs of proteins having 10-30% similarity, the average accuracy of alignments generated by STRSWer is 15% higher than that achieved with the Smith-Waterman algorithm.     

Speedup bioinformatics applications on multicore-based processor using vectorizing and multithreading strategies

Many computational intensive bioinformatics software, such as multiple sequence alignment, population structure analysis, etc., written in C/C++ are not multicore-aware. A multicore processor is an emerging CPU technology that combines two or more independent processors into a single package. The Single Instruction Multiple Data-stream (SIMD) paradigm is heavily utilized in this class of processors. Nevertheless, most popular compilers including Microsoft Visual C/C++ 6.0, x86 gnu C-compiler gcc do not automatically create SIMD code which can fully utilize the advancement of these processors. To harness the power of the new multicore architecture certain compiler techniques must be considered. This paper presents a generic compiling strategy to assist the compiler in improving the performance of bioinformatics applications written in C/C++. The proposed framework contains 2 main steps: multithreading and vectorizing strategies. After following the strategies, the application can achieve higher speedup by taking the advantage of multicore architecture technology. Due to the extremely fast interconnection networking among multiple cores, it is suggested that the proposed optimization could be more appropriate than making use of parallelization on a small cluster computer which has larger network latency and lower bandwidth.