Claudio Reggiani

This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.

Background - Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders.

Methods - Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data… Show more

Background - Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders.

Methods - Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play.

Results - Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse. These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients’ clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories.

Conclusions - While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases. Show less

Bioinformatics is an evolving field combining, statistics, mathematics and engineering in order to interpret biological data. This vast domain deals with large datasets which need to be efficiently processed. The Big Data paradigm will be analysed in this context, presenting its advantages and shortcomings over traditional computational methods. This paper describes the implementation of a tool capable of standardizing the vast formats in which biological data is represented leveraging… Show more

Bioinformatics is an evolving field combining, statistics, mathematics and engineering in order to interpret biological data. This vast domain deals with large datasets which need to be efficiently processed. The Big Data paradigm will be analysed in this context, presenting its advantages and shortcomings over traditional computational methods. This paper describes the implementation of a tool capable of standardizing the vast formats in which biological data is represented leveraging distributed systems such as Apache Spark. Show less

High-dimensional datasets include useful information for prediction purposes, but redundancy of features and noise affect negatively classifier performance. Feature selection algorithms are employed to tackle the curse of dimensionality and improve performance by selecting a subset of features. In this paper we address the design and implementation of minimum Redundancy Maximum Relevance feature selection algorithm using MapReduce paradigm, through the Apache Hadoop framework. We report… Show more

High-dimensional datasets include useful information for prediction purposes, but redundancy of features and noise affect negatively classifier performance. Feature selection algorithms are employed to tackle the curse of dimensionality and improve performance by selecting a subset of features. In this paper we address the design and implementation of minimum Redundancy Maximum Relevance feature selection algorithm using MapReduce paradigm, through the Apache Hadoop framework. We report preliminary results on the scalability of our algorithm Show less