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Eugenia Migliavacca

dblp:25/6757 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
0000-0003-1883-1972ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › epigenomics
ChIP-seq analysis
0.212014
Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data · Bioinform. 2014
Bioinformatics and computational biology
epigenomics
0.212014
Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data · Bioinform. 2014
Bioinformatics and computational biology › sequence analysis › sequencing data processing
sequencing quality control
0.212014
Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data · Bioinform. 2014
Bioinformatics and computational biology
biological database
0.012001
MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001
Bioinformatics and computational biology › structural bioinformatics
protein structure database
0.012001
MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001
Data models and query languages
data description language
0.012001
MDB: a database system utilizing automatic construction of modules and STAR-derived universal language · Bioinform. 2001

Methods — techniques the papers use, named apart from their topics

clonal read filtering · 0.2dictionary-based data design · 0.1automatic relational database construction · 0.1
YearPublicationVenuePosition
2014 Identification and removal of low-complexity sites in allele-specific analysis of ChIP-seq data
abstract
MOTIVATION: High-throughput sequencing technologies enable the genome-wide analysis of the impact of genetic variation on molecular phenotypes at unprecedented resolution. However, although powerful, these technologies can also introduce unexpected artifacts. RESULTS: We investigated the impact of library amplification bias on the identification of allele-specific (AS) molecular events from high-throughput sequencing data derived from chromatin immunoprecipitation assays (ChIP-seq). Putative AS DNA binding activity for RNA polymerase II was determined using ChIP-seq data derived from lymphoblastoid cell lines of two parent-daughter trios. We found that, at high-sequencing depth, many significant AS binding sites suffered from an amplification bias, as evidenced by a larger number of clonal reads representing one of the two alleles. To alleviate this bias, we devised an amplification bias detection strategy, which filters out sites with low read complexity and sites featuring a significant excess of clonal reads. This method will be useful for AS analyses involving ChIP-seq and other functional sequencing assays. AVAILABILITY: The R package abs filter for library clonality simulations and detection of amplification-biased sites is available from http://updepla1srv1.epfl.ch/waszaks/absfilter
Sebastian M. Waszak, Helena Kilpinen, Andreas R. Gschwind, Andrea Orioli, Sunil K. Raghav, Robert M. Witwicki, Eugenia Migliavacca, Alisa Yurovsky, Tuuli Lappalainen, Nouria Hernandez, Alexandre Reymond, Emmanouil T. Dermitzakis, Bart Deplancke
Bioinform.7
2001 MDB: a database system utilizing automatic construction of modules and STAR-derived universal language
abstract
MOTIVATION: The value of information greatly increases if stored in databases. The objective was to construct a multi-purpose database system primarily designed to store and provide access to three-dimensional structures of biological molecules including theoretical models. RESULTS: A dictionary defining data format and structure for three-dimensional models of biological molecules (MDB dictionary) was developed. The dictionary was written using universal, standardized data description language. This language can be applied to describe data with no restrictions on their origin or type, including metadata. Thus both the data definitions (format) and database descriptions are created using the uniform language and processed with universal software. A database and data design technique that allowed use of dictionaries to automatically construct relational databases was developed. This technique was employed to construct the MDB database system. Data design developed and applied in the MDB project makes it possible to carry out data curation utilizing the database engine to identify errors. It also allows storage and query of data at different levels of consistency with the standard format specifications, i.e. both the correctly formatted data, and data that requires further curation. AVAILABILITY: The MDB dictionary is available at http://www.gwer.ch/proteinstructure/mdb and as part of the PDB resources at http://pdb.rutgers.edu/mmcif/.
Eugenia Migliavacca, Alexei A. Adzhubei, Manuel C. Peitsch
Bioinform.1