Aneuploidy and recombination in the human preimplantation embryo. Copy number variation analysis and genome-wide polymorphism genotyping.

RESEARCH QUESTION: What are the incidence and patterns of meiotic trisomies and recombination separately and in relation to each other at the blastocyst stage via single nucleotide polymorphism genotyping combined with array comparative genomic hybridization. DESIGN: Single nucleotide polymorphism microarrays were carried out on a total of 1442 blastocyst stage embryos derived from 268 fertile couples undergoing preimplantation genetic diagnosis for the purposes of avoiding transmittance of known single gene disorders to their offspring; 24-chromosome aneuploidy screening via array comparative genomic hybridization was carried out in parallel. RESULTS: One hundred per cent of meiotic trisomies identified in these embryos were of maternal origin and their incidence increased significantly with advancing maternal age (P < 0.0001). A total of 55.8% of meiotic trisomies were meiosis I-type and 44.2% were meiosis II-type. Certain chromosomes were affected more by meiosis I-type errors, whereas others experienced more meiosis II-type errors. A detailed recombination analysis was carried out for 11,476 chromosomes and 17,763 recombination events were recorded. The average number of recombination sites was 24.0 ± 0.3 for male meiosis and 41.2 ± 0.6 for female meiosis (autosomes only). Sex-specific differences were observed in the locations of recombination sites. Comparative analysis conducted between 190 euploid embryos and 69 embryos presenting maternal meiotic trisomies showed similar recombination rates (P = 0.425) and non-recombinant chromatid rates (P = 0.435) between the two categories; differences, however, were observed when analysing embryos affected with specific maternal meiotic trisomies. CONCLUSIONS: This study yielded unique data concerning recombination and the origin of aneuploidies observed during the first few days of life and provides a novel insight into these important biological processes.

The ISCB Student Council Internship Program: Expanding computational biology capacity worldwide.

Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one's field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one's research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

MLL1 Inhibition Reprograms Epiblast Stem Cells to Naive Pluripotency.

The interconversion between naive and primed pluripotent states is accompanied by drastic epigenetic rearrangements. However, it is unclear whether intrinsic epigenetic events can drive reprogramming to naive pluripotency or if distinct chromatin states are instead simply a reflection of discrete pluripotent states. Here, we show that blocking histone H3K4 methyltransferase MLL1 activity with the small-molecule inhibitor MM-401 reprograms mouse epiblast stem cells (EpiSCs) to naive pluripotency. This reversion is highly efficient and synchronized, with more than 50% of treated EpiSCs exhibiting features of naive embryonic stem cells (ESCs) within 3 days. Reverted ESCs reactivate the silenced X chromosome and contribute to embryos following blastocyst injection, generating germline-competent chimeras. Importantly, blocking MLL1 leads to global redistribution of H3K4me1 at enhancers and represses lineage determinant factors and EpiSC markers, which indirectly regulate ESC transcription circuitry. These findings show that discrete perturbation of H3K4 methylation is sufficient to drive reprogramming to naive pluripotency.

Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics.

In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity.

Utility of RNA-seq and GPMDB protein observation frequency for improving the sensitivity of protein identification by tandem MS.

Tandem mass spectrometry (MS/MS) followed by database search is the method of choice for protein identification in proteomic studies. Database searching methods employ spectral matching algorithms and statistical models to identify and quantify proteins in a sample. In general, these methods do not utilize any information other than spectral data for protein identification. However, considering the wealth of external data available for many biological systems, analysis methods can incorporate such information to improve the sensitivity of protein identification. In this study, we present a method to utilize Global Proteome Machine Database identification frequencies and RNA-seq transcript abundances to adjust the confidence scores of protein identifications. The method described is particularly useful for samples with low-to-moderate proteome coverage (i.e., <2000-3000 proteins), where we observe up to an 8% improvement in the number of proteins identified at a 1% false discovery rate.

Highlights from the 1st ISCB Latin American Student Council Symposium 2014. Introduction.

This report summarizes the scientific content and activities of the first edition of the Latin American Symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Third Latin American conference from the International Society for Computational Biology (ISCB-LA 2014) in Belo Horizonte, Brazil, on October 27, 2014.

Fast parallel tandem mass spectral library searching using GPU hardware acceleration.

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.