1,227 research outputs found
Optimizing a Massive Parallel Sequencing Workflow for Quantitative miRNA Expression Analysis
BACKGROUND: Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. PRIMARY FINDINGS: A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. CONCLUSIONS: The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis
Circular sequence comparison: algorithms and applications
Background: Sequence comparison is a fundamental step in many important tasks in bioinformatics; from phylogenetic reconstruction to the reconstruction of genomes. Traditional algorithms for measuring approximation in sequence comparison are based on the notions of distance or similarity, and are generally computed through sequence alignment techniques. As circular molecular structure is a common phenomenon in nature, a caveat of the adaptation of alignment techniques for circular sequence comparison is that they are computationally expensive, requiring from super-quadratic to cubic time in the length of the sequences. Results: In this paper, we introduce a new distance measure based on q-grams, and show how it can be applied effectively and computed efficiently for circular sequence comparison. Experimental results, using real DNA, RNA, and protein sequences as well as synthetic data, demonstrate orders-of-magnitude superiority of our approach in terms of efficiency, while maintaining an accuracy very competitive to the state of the art
Longest Common Prefixes with -Errors and Applications
Although real-world text datasets, such as DNA sequences, are far from being
uniformly random, average-case string searching algorithms perform
significantly better than worst-case ones in most applications of interest. In
this paper, we study the problem of computing the longest prefix of each suffix
of a given string of length over a constant-sized alphabet that occurs
elsewhere in the string with -errors. This problem has already been studied
under the Hamming distance model. Our first result is an improvement upon the
state-of-the-art average-case time complexity for non-constant and using
only linear space under the Hamming distance model. Notably, we show that our
technique can be extended to the edit distance model with the same time and
space complexities. Specifically, our algorithms run in time on average using space. We show that our
technique is applicable to several algorithmic problems in computational
biology and elsewhere
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination
We consider the question of Gaussian mean testing, a fundamental task in
high-dimensional distribution testing and signal processing, subject to
adversarial corruptions of the samples. We focus on the relative power of
different adversaries, and show that, in contrast to the common wisdom in
robust statistics, there exists a strict separation between adaptive
adversaries (strong contamination) and oblivious ones (weak contamination) for
this task. Specifically, we resolve both the information-theoretic and
computational landscapes for robust mean testing. In the exponential-time
setting, we establish the tight sample complexity of testing
against , where , with an
-fraction of adversarial corruptions, to be while the
complexity against adaptive adversaries is which is strictly worse
for a large range of vanishing . To the best of our
knowledge, ours is the first separation in sample complexity between the strong
and weak contamination models.
In the polynomial-time setting, we close a gap in the literature by providing
a polynomial-time algorithm against adaptive adversaries achieving the above
sample complexity , and a low-degree lower bound (which
complements an existing reduction from planted clique) suggesting that all
efficient algorithms require this many samples, even in the oblivious-adversary
setting.Comment: To appear in FOCS 202
Detecting genomic indel variants with exact breakpoints in single- and paired-end sequencing data using SplazerS
Motivation: The reliable detection of genomic variation in resequencing data is still a major challenge, especially for variants larger than a few base pairs. Sequencing reads crossing boundaries of structural variation carry the potential for their identification, but are difficult to map.
Results: Here we present a method for ‘split’ read mapping, where prefix and suffix match of a read may be interrupted by a longer gap in the read-to-reference alignment. We use this method to accurately detect medium-sized insertions and long deletions with precise breakpoints in genomic resequencing data. Compared with alternative split mapping methods, SplazerS significantly improves sensitivity for detecting large indel events, especially in variant-rich regions. Our method is robust in the presence of sequencing errors as well as alignment errors due to genomic mutations/divergence, and can be used on reads of variable lengths. Our analysis shows that SplazerS is a versatile tool applicable to unanchored or single-end as well as anchored paired-end reads. In addition, application of SplazerS to targeted resequencing data led to the interesting discovery of a complete, possibly functional gene retrocopy variant.
Availability: SplazerS is available from http://www.seqan.de/projects/ splazers
Phylogenetic comparative assembly
Husemann P, Stoye J. Phylogenetic Comparative Assembly. Algorithms for Molecular Biology. 2010;5(1): 3.BACKGROUND:Recent high throughput sequencing technologies are capable of generating a huge amount of data for bacterial genome sequencing projects. Although current sequence assemblers successfully merge the overlapping reads, often several contigs remain which cannot be assembled any further. It is still costly and time consuming to close all the gaps in order to acquire the whole genomic sequence. RESULTS:Here we propose an algorithm that takes several related genomes and their phylogenetic relationships into account to create a graph that contains the likelihood for each pair of contigs to be adjacent. Subsequently, this graph can be used to compute a layout graph that shows the most promising contig adjacencies in order to aid biologists in finishing the complete genomic sequence. The layout graph shows unique contig orderings where possible, and the best alternatives where necessary. CONCLUSIONS:Our new algorithm for contig ordering uses sequence similarity as well as phylogenetic information to estimate adjacencies of contigs. An evaluation of our implementation shows that it performs better than recent approaches while being much faster at the same tim
Parallel Natural Language Parsing: From Analysis to Speedup
Electrical Engineering, Mathematics and Computer Scienc
Effective Instance Matching for Heterogeneous Structured Data
One main problem towards the effective usage of structured data is instance matching, where the goal is to find instance representations referring to the same real-world thing. In this book we investigate how to effectively match Heterogeneous structured data. We evaluate our approaches against the latest baselines. The results show advances beyond the state-of-the-art
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