27,682 research outputs found
Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version
Although computationally aligning sequence is a crucial step in the vast
majority of comparative genomics studies our understanding of alignment biases
still needs to be improved. To infer true structural or homologous regions
computational alignments need further evaluation. It has been shown that the
accuracy of aligned positions can drop substantially in particular around gaps.
Here we focus on re-evaluation of score-based alignments with affine gap
penalty costs. We exploit their relationships with pair hidden Markov models
and develop efficient algorithms by which to identify gaps which are
significant in terms of length and multiplicity. We evaluate our statistics
with respect to the well-established structural alignments from SABmark and
find that indel reliability substantially increases with their significance in
particular in worst-case twilight zone alignments. This points out that our
statistics can reliably complement other methods which mostly focus on the
reliability of match positions.Comment: 17 pages, 7 figure
A two-base encoded DNA sequence alignment problem in computational biology
The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics. The primary objective of the "sequence alignment" problem is to search for a new algorithm that facilitates the use of two-base encoded data for large-scale re-sequencing projects. This algorithm should be able to perform local sequence alignment as well as error detection and correction in a reliable and systematic manner, enabling the direct comparison of encoded DNA sequence reads to a candidate reference DNA sequence.
We will first briefly review two well-known sequence alignment approaches and provide a rudimentary improvement for implementation on parallel systems. Then, we carefully examin a unique sequencing technique known as the SOLiDTM System that can be implemented, and follow by the results from the global and local sequence alignment.
In this report, the team presents an explanation of the algorithms for color space sequence data from the high-throughput re-sequencing technology and a theoretical parallel approach to the dynamic programming method for global and local alignment. The combination of the di-base approach and dynamic programming provides a possible viewpoint for large-scale re-sequencing projects. We anticipate the use of distributed computing to be the next-generation engine for large-scale problems like such
SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner
To tackle the exponentially increasing throughput of Next-Generation
Sequencing (NGS), most of the existing short-read aligners can be configured to
favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging
the computational power of both CPU and GPU with optimized algorithms, delivers
high speed and sensitivity simultaneously. Compared with widely adopted
aligners including BWA, Bowtie2, SeqAlto, GEM and GPU-based aligners including
BarraCUDA and CUSHAW, SOAP3-dp is two to tens of times faster, while
maintaining the highest sensitivity and lowest false discovery rate (FDR) on
Illumina reads with different lengths. Transcending its predecessor SOAP3,
which does not allow gapped alignment, SOAP3-dp by default tolerates alignment
similarity as low as 60 percent. Real data evaluation using human genome
demonstrates SOAP3-dp's power to enable more authentic variants and longer
Indels to be discovered. Fosmid sequencing shows a 9.1 percent FDR on newly
discovered deletions. SOAP3-dp natively supports BAM file format and provides a
scoring scheme same as BWA, which enables it to be integrated into existing
analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and
Tianhe-1A.Comment: 21 pages, 6 figures, submitted to PLoS ONE, additional files
available at "https://www.dropbox.com/sh/bhclhxpoiubh371/O5CO_CkXQE".
Comments most welcom
Topological network alignment uncovers biological function and phylogeny
Sequence comparison and alignment has had an enormous impact on our
understanding of evolution, biology, and disease. Comparison and alignment of
biological networks will likely have a similar impact. Existing network
alignments use information external to the networks, such as sequence, because
no good algorithm for purely topological alignment has yet been devised. In
this paper, we present a novel algorithm based solely on network topology, that
can be used to align any two networks. We apply it to biological networks to
produce by far the most complete topological alignments of biological networks
to date. We demonstrate that both species phylogeny and detailed biological
function of individual proteins can be extracted from our alignments.
Topology-based alignments have the potential to provide a completely new,
independent source of phylogenetic information. Our alignment of the
protein-protein interaction networks of two very different species--yeast and
human--indicate that even distant species share a surprising amount of network
topology with each other, suggesting broad similarities in internal cellular
wiring across all life on Earth.Comment: Algorithm explained in more details. Additional analysis adde
Global Network Alignment
Motivation: High-throughput methods for detecting molecular interactions have lead to a plethora of biological network data with much more yet to come, stimulating the development of techniques for biological network alignment. Analogous to sequence alignment, efficient and reliable network alignment methods will improve our understanding of biological systems. Network alignment is computationally hard. Hence, devising efficient network alignment heuristics is currently one of the foremost challenges in computational biology. 

Results: We present a superior heuristic network alignment algorithm, called Matching-based GRAph ALigner (M-GRAAL), which can process and integrate any number and type of similarity measures between network nodes (e.g., proteins), including, but not limited to, any topological network similarity measure, sequence similarity, functional similarity, and structural similarity. This is efficient in resolving ties in similarity measures and in finding a combination of similarity measures yielding the largest biologically sound alignments. When used to align protein-protein interaction (PPI) networks of various species, M-GRAAL exposes the largest known functional and contiguous regions of network similarity. Hence, we use M-GRAAL’s alignments to predict functions of un-annotated proteins in yeast, human, and bacteria _C. jejuni_ and _E. coli_. Furthermore, using M-GRAAL to compare PPI networks of different herpes viruses, we reconstruct their phylogenetic relationship and our phylogenetic tree is the same as sequenced-based one
An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.
Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks
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