Angel Pizarro

Bio: Angel Pizarro is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Proteomics Standards Initiative & Mass spectrometry data format. The author has an hindex of 21, co-authored 39 publications receiving 2997 citations. Previous affiliations of Angel Pizarro include Amazon.com & Genomics Institute of the Novartis Research Foundation.

Design and implementation of microarray gene expression markup language (MAGE-ML)

Abstract: Background Meaningful exchange of microarray data is currently difficult because it is rare that published data provide sufficient information depth or are even in the same format from one publication to another. Only when data can be easily exchanged will the entire biological community be able to derive the full benefit from such microarray studies.

Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)

Abstract: Motivation: A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously. Results: We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription–polymerase chain reaction (RT–PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability. Availability: The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine ( http://cbil.upenn.edu/RUM). Contact:ggrant@pcbi.upenn.edu; epierce@mail.med.upenn.edu Supplementary Information:The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.

CircaDB: a database of mammalian circadian gene expression profiles

Abstract: CircaDB (http://circadb.org) is a new database of circadian transcriptional profiles from time course expression experiments from mice and humans. Each transcript’s expression was evaluated by three separate algorithms, JTK_Cycle, Lomb Scargle and DeLichtenberg. Users can query the gene annotations using simple and powerful full text search terms, restrict results to specific data sets and provide probability thresholds for each algorithm. Visualizations of the data are intuitive charts that convey profile information more effectively than a table of probabilities. The CircaDB web application is open source and available at http:// github.com/itmat/circadb.

STAR: ultrafast universal RNA-seq aligner

Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

HISAT: a fast spliced aligner with low memory requirements

Abstract: HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

Abstract: TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

2016 update of the PRIDE database and its related tools

Abstract: The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data Since the beginning of 2014, PRIDE Archive (http://wwwebiacuk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013 PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month) We outline some statistics on the current PRIDE Archive data contents We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive