23,622 research outputs found
A novel and well-defined benchmarking method for second generation read mapping
Background Second generation sequencing technologies yield DNA sequence data
at ultra high-throughput. Common to most biological applications is a mapping
of the reads to an almost identical or highly similar reference genome. The
assessment of the quality of read mapping results is not straightforward and
has not been formalized so far. Hence, it has not been easy to compare
different read mapping approaches in a unified way and to determine which
program is the best for what task. Results We present a new benchmark method,
called Rabema (Read Alignment BEnchMArk), for read mappers. It consists of a
strict definition of the read mapping problem and of tools to evaluate the
result of arbitrary read mappers supporting the SAM output format. Conclusions
We show the usefulness of the benchmark program by performing a comparison of
popular read mappers. The tools supporting the benchmark are licensed under
the GPL and available from http://www.seqan.de/projects/rabema.html
Understand Your Chains: Towards Performance Profile-based Network Service Management
Allocating resources to virtualized network functions and services to meet
service level agreements is a challenging task for NFV management and
orchestration systems. This becomes even more challenging when agile
development methodologies, like DevOps, are applied. In such scenarios,
management and orchestration systems are continuously facing new versions of
functions and services which makes it hard to decide how much resources have to
be allocated to them to provide the expected service performance. One solution
for this problem is to support resource allocation decisions with performance
behavior information obtained by profiling techniques applied to such network
functions and services.
In this position paper, we analyze and discuss the components needed to
generate such performance behavior information within the NFV DevOps workflow.
We also outline research questions that identify open issues and missing pieces
for a fully integrated NFV profiling solution. Further, we introduce a novel
profiling mechanism that is able to profile virtualized network functions and
entire network service chains under different resource constraints before they
are deployed on production infrastructure.Comment: Submitted to and accepted by the European Workshop on Software
Defined Networks (EWSDN) 201
RNF: a general framework to evaluate NGS read mappers
Aligning reads to a reference sequence is a fundamental step in numerous
bioinformatics pipelines. As a consequence, the sensitivity and precision of
the mapping tool, applied with certain parameters to certain data, can
critically affect the accuracy of produced results (e.g., in variant calling
applications). Therefore, there has been an increasing demand of methods for
comparing mappers and for measuring effects of their parameters.
Read simulators combined with alignment evaluation tools provide the most
straightforward way to evaluate and compare mappers. Simulation of reads is
accompanied by information about their positions in the source genome. This
information is then used to evaluate alignments produced by the mapper.
Finally, reports containing statistics of successful read alignments are
created.
In default of standards for encoding read origins, every evaluation tool has
to be made explicitly compatible with the simulator used to generate reads. In
order to solve this obstacle, we have created a generic format RNF (Read Naming
Format) for assigning read names with encoded information about original
positions.
Futhermore, we have developed an associated software package RNF containing
two principal components. MIShmash applies one of popular read simulating tools
(among DwgSim, Art, Mason, CuReSim etc.) and transforms the generated reads
into RNF format. LAVEnder evaluates then a given read mapper using simulated
reads in RNF format. A special attention is payed to mapping qualities that
serve for parametrization of ROC curves, and to evaluation of the effect of
read sample contamination
CLEVER: Clique-Enumerating Variant Finder
Next-generation sequencing techniques have facilitated a large scale analysis
of human genetic variation. Despite the advances in sequencing speeds, the
computational discovery of structural variants is not yet standard. It is
likely that many variants have remained undiscovered in most sequenced
individuals. Here we present a novel internal segment size based approach,
which organizes all, including also concordant reads into a read alignment
graph where max-cliques represent maximal contradiction-free groups of
alignments. A specifically engineered algorithm then enumerates all max-cliques
and statistically evaluates them for their potential to reflect insertions or
deletions (indels). For the first time in the literature, we compare a large
range of state-of-the-art approaches using simulated Illumina reads from a
fully annotated genome and present various relevant performance statistics. We
achieve superior performance rates in particular on indels of sizes 20--100,
which have been exposed as a current major challenge in the SV discovery
literature and where prior insert size based approaches have limitations. In
that size range, we outperform even split read aligners. We achieve good
results also on real data where we make a substantial amount of correct
predictions as the only tool, which complement the predictions of split-read
aligners. CLEVER is open source (GPL) and available from
http://clever-sv.googlecode.com.Comment: 30 pages, 8 figure
EFICAz²: enzyme function inference by a combined approach enhanced by machine learning
©2009 Arakaki et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm
Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept
Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context
gMark: Schema-Driven Generation of Graphs and Queries
Massive graph data sets are pervasive in contemporary application domains.
Hence, graph database systems are becoming increasingly important. In the
experimental study of these systems, it is vital that the research community
has shared solutions for the generation of database instances and query
workloads having predictable and controllable properties. In this paper, we
present the design and engineering principles of gMark, a domain- and query
language-independent graph instance and query workload generator. A core
contribution of gMark is its ability to target and control the diversity of
properties of both the generated instances and the generated workloads coupled
to these instances. Further novelties include support for regular path queries,
a fundamental graph query paradigm, and schema-driven selectivity estimation of
queries, a key feature in controlling workload chokepoints. We illustrate the
flexibility and practical usability of gMark by showcasing the framework's
capabilities in generating high quality graphs and workloads, and its ability
to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL:
http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on
Knowledge and Data Engineering 201
The mapping task and its various applications in next-generation sequencing
The aim of this thesis is the development and benchmarking of
computational methods for the analysis of high-throughput data from
tiling arrays and next-generation sequencing. Tiling arrays have been
a mainstay of genome-wide transcriptomics, e.g., in the identification
of functional elements in the human genome. Due to limitations of
existing methods for the data analysis of this data, a novel
statistical approach is presented that identifies expressed segments
as significant differences from the background distribution and thus
avoids dataset-specific parameters. This method detects differentially
expressed segments in biological data with significantly lower false
discovery rates and equivalent sensitivities compared to commonly used
methods. In addition, it is also clearly superior in the recovery of
exon-intron structures. Moreover, the search for local accumulations
of expressed segments in tiling array data has led to the
identification of very large expressed regions that may constitute a
new class of macroRNAs.
This thesis proceeds with next-generation sequencing for which various
protocols have been devised to study genomic, transcriptomic, and
epigenomic features. One of the first crucial steps in most NGS data
analyses is the mapping of sequencing reads to a reference
genome. This work introduces algorithmic methods to solve the mapping
tasks for three major NGS protocols: DNA-seq, RNA-seq, and
MethylC-seq. All methods have been thoroughly benchmarked and
integrated into the segemehl mapping suite.
First, mapping of DNA-seq data is facilitated by the core mapping
algorithm of segemehl. Since the initial publication, it has been
continuously updated and expanded. Here, extensive and reproducible
benchmarks are presented that compare segemehl to state-of-the-art
read aligners on various data sets. The results indicate that it is
not only more sensitive in finding the optimal alignment with respect
to the unit edit distance but also very specific compared to most
commonly used alternative read mappers. These advantages are
observable for both real and simulated reads, are largely independent
of the read length and sequencing technology, but come at the cost of
higher running time and memory consumption.
Second, the split-read extension of segemehl, presented by Hoffmann,
enables the mapping of RNA-seq data, a computationally more difficult
form of the mapping task due to the occurrence of splicing. Here, the
novel tool lack is presented, which aims to recover missed RNA-seq
read alignments using de novo splice junction information. It
performs very well in benchmarks and may thus be a beneficial
extension to RNA-seq analysis pipelines.
Third, a novel method is introduced that facilitates the mapping of
bisulfite-treated sequencing data. This protocol is considered the
gold standard in genome-wide studies of DNA methylation, one of the
major epigenetic modifications in animals and plants. The treatment of
DNA with sodium bisulfite selectively converts unmethylated cytosines
to uracils, while methylated ones remain unchanged. The bisulfite
extension developed here performs seed searches on a collapsed
alphabet followed by bisulfite-sensitive dynamic programming
alignments. Thus, it is insensitive to bisulfite-related mismatches
and does not rely on post-processing, in contrast to other methods. In
comparison to state-of-the-art tools, this method achieves
significantly higher sensitivities and performs time-competitive in
mapping millions of sequencing reads to vertebrate
genomes. Remarkably, the increase in sensitivity does not come at the
cost of decreased specificity and thus may finally result in a better
performance in calling the methylation rate.
Lastly, the potential of mapping strategies for de novo genome
assemblies is demonstrated with the introduction of a new guided
assembly procedure. It incorporates mapping as major component and
uses the additional information (e.g., annotation) as guide. With this
method, the complete mitochondrial genome of Eulimnogammarus verrucosus has been
successfully assembled even though the sequencing library has been
heavily dominated by nuclear DNA.
In summary, this thesis introduces algorithmic methods that
significantly improve the analysis of tiling array, DNA-seq, RNA-seq,
and MethylC-seq data, and proposes standards for benchmarking NGS read
aligners. Moreover, it presents a new guided assembly procedure that
has been successfully applied in the de novo assembly of a
crustacean mitogenome.Diese Arbeit befasst sich mit der Entwicklung und dem Benchmarken von
Verfahren zur Analyse von Daten aus Hochdurchsatz-Technologien, wie
Tiling Arrays oder Hochdurchsatz-Sequenzierung. Tiling Arrays bildeten
lange Zeit die Grundlage fĂĽr die genomweite Untersuchung des
Transkriptoms und kamen beispielsweise bei der Identifizierung
funktioneller Elemente im menschlichen Genom zum Einsatz. In dieser
Arbeit wird ein neues statistisches Verfahren zur Auswertung von
Tiling Array-Daten vorgestellt. Darin werden Segmente als exprimiert
klassifiziert, wenn sich deren Signale signifikant von der
Hintergrundverteilung unterscheiden. Dadurch werden keine auf den
Datensatz abgestimmten Parameterwerte benötigt. Die hier
vorgestellte Methode erkennt differentiell exprimierte Segmente in
biologischen Daten bei gleicher Sensitivität mit geringerer
Falsch-Positiv-Rate im Vergleich zu den derzeit hauptsächlich
eingesetzten Verfahren. Zudem ist die Methode bei der Erkennung von
Exon-Intron Grenzen präziser. Die Suche nach Anhäufungen
exprimierter Segmente hat darĂĽber hinaus zur Entdeckung von sehr
langen Regionen geführt, welche möglicherweise eine neue
Klasse von macroRNAs darstellen.
Nach dem Exkurs zu Tiling Arrays konzentriert sich diese Arbeit nun
auf die Hochdurchsatz-Sequenzierung, fĂĽr die bereits verschiedene
Sequenzierungsprotokolle zur Untersuchungen des Genoms, Transkriptoms
und Epigenoms etabliert sind. Einer der ersten und entscheidenden
Schritte in der Analyse von Sequenzierungsdaten stellt in den meisten
Fällen das Mappen dar, bei dem kurze Sequenzen (Reads) auf ein
groĂźes Referenzgenom aligniert werden. Die vorliegende Arbeit
stellt algorithmische Methoden vor, welche das Mapping-Problem fĂĽr
drei wichtige Sequenzierungsprotokolle (DNA-Seq, RNA-Seq und
MethylC-Seq) lösen. Alle Methoden wurden ausführlichen
Benchmarks unterzogen und sind in der segemehl-Suite integriert.
Als Erstes wird hier der Kern-Algorithmus von segemehl vorgestellt,
welcher das Mappen von DNA-Sequenzierungsdaten ermöglicht. Seit
der ersten Veröffentlichung wurde dieser kontinuierlich optimiert
und erweitert. In dieser Arbeit werden umfangreiche und auf
Reproduzierbarkeit bedachte Benchmarks präsentiert, in denen
segemehl auf zahlreichen Datensätzen mit bekannten
Mapping-Programmen verglichen wird. Die Ergebnisse zeigen, dass
segemehl nicht nur sensitiver im Auffinden von optimalen Alignments
bezĂĽglich der Editierdistanz sondern auch sehr spezifisch im
Vergleich zu anderen Methoden ist. Diese Vorteile sind in realen und
simulierten Daten unabhängig von der Sequenzierungstechnologie
oder der Länge der Reads erkennbar, gehen aber zu Lasten einer
längeren Laufzeit und eines höheren Speicherverbrauchs.
Als Zweites wird das Mappen von RNA-Sequenzierungsdaten untersucht,
welches bereits von der Split-Read-Erweiterung von segemehl
unterstĂĽtzt wird. Aufgrund von SpleiĂźen ist diese Form des
Mapping-Problems rechnerisch aufwendiger. In dieser Arbeit wird das
neue Programm lack vorgestellt, welches darauf abzielt, fehlende
Read-Alignments mit Hilfe von de novo SpleiĂź-Information zu
finden. Es erzielt hervorragende Ergebnisse und stellt somit eine
sinnvolle Ergänzung zu Analyse-Pipelines für
RNA-Sequenzierungsdaten dar.
Als Drittes wird eine neue Methode zum Mappen von Bisulfit-behandelte
Sequenzierungsdaten vorgestellt. Dieses Protokoll gilt als
Goldstandard in der genomweiten Untersuchung der DNA-Methylierung,
einer der wichtigsten epigenetischen Modifikationen in Tieren und
Pflanzen. Dabei wird die DNA vor der Sequenzierung mit Natriumbisulfit
behandelt, welches selektiv nicht methylierte Cytosine zu Uracilen
konvertiert, während Methylcytosine davon unberührt
bleiben. Die hier vorgestellte Bisulfit-Erweiterung fĂĽhrt die
Seed-Suche auf einem reduziertem Alphabet durch und verifiziert die
erhaltenen Treffer mit einem auf dynamischer Programmierung
basierenden Bisulfit-sensitiven Alignment-Algorithmus. Das verwendete
Verfahren ist somit unempfindlich gegenĂĽber
Bisulfit-Konvertierungen und erfordert im Gegensatz zu anderen
Verfahren keine weitere Nachverarbeitung. Im Vergleich zu aktuell
eingesetzten Programmen ist die Methode sensitiver und benötigt
eine vergleichbare Laufzeit beim Mappen von Millionen von Reads auf
große Genome. Bemerkenswerterweise wird die erhöhte
Sensitivität bei gleichbleibend guter Spezifizität
erreicht. Dadurch könnte diese Methode somit auch bessere
Ergebnisse bei der präzisen Bestimmung der Methylierungsraten
erreichen.
SchlieĂźlich wird noch das Potential von Mapping-Strategien fĂĽr
Assemblierungen mit der EinfĂĽhrung eines neuen,
Kristallisation-genanntes Verfahren zur unterstĂĽtzten
Assemblierung aufgezeigt. Es enthält Mapping als Hauptbestandteil
und nutzt Zusatzinformation (z.B. Annotationen) als
Unterstützung. Dieses Verfahren ermöglichte die erfolgreiche
Assemblierung des kompletten mitochondrialen Genoms von Eulimnogammarus verrucosus trotz
einer vorwiegend aus nukleärer DNA bestehenden genomischen
Bibliothek.
Zusammenfassend stellt diese Arbeit algorithmische Methoden vor,
welche die Analysen von Tiling Array, DNA-Seq, RNA-Seq und MethylC-Seq
Daten signifikant verbessern. Es werden zudem Standards fĂĽr den
Vergleich von Programmen zum Mappen von Daten der
Hochdurchsatz-Sequenzierung vorgeschlagen. DarĂĽber hinaus wird ein
neues Verfahren zur unterstĂĽtzten Genom-Assemblierung vorgestellt,
welches erfolgreich bei der de novo-Assemblierung eines
mitochondrialen Krustentier-Genoms eingesetzt wurde
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