Developmental validation of the MiSeq FGx Forensic Genomics System for Targeted Next Generation Sequencing in Forensic DNA Casework and Database Laboratories.

Human DNA profiling using PCR at polymorphic short tandem repeat (STR) loci followed by capillary electrophoresis (CE) size separation and length-based allele typing has been the standard in the forensic community for over 20 years. Over the last decade, Next-Generation Sequencing (NGS) matured rapidly, bringing modern advantages to forensic DNA analysis. The MiSeq FGx™ Forensic Genomics System, comprised of the ForenSeq™ DNA Signature Prep Kit, MiSeq FGx™ Reagent Kit, MiSeq FGx™ instrument and ForenSeq™ Universal Analysis Software, uses PCR to simultaneously amplify up to 231 forensic loci in a single multiplex reaction. Targeted loci include Amelogenin, 27 common, forensic autosomal STRs, 24 Y-STRs, 7 X-STRs and three classes of single nucleotide polymorphisms (SNPs). The ForenSeq™ kit includes two primer sets: Amelogenin, 58 STRs and 94 identity informative SNPs (iiSNPs) are amplified using DNA Primer Set A (DPMA; 153 loci); if a laboratory chooses to generate investigative leads using DNA Primer Set B, amplification is targeted to the 153 loci in DPMA plus 22 phenotypic informative (piSNPs) and 56 biogeographical ancestry SNPs (aiSNPs). High-resolution genotypes, including detection of intra-STR sequence variants, are semi-automatically generated with the ForenSeq™ software. This system was subjected to developmental validation studies according to the 2012 Revised SWGDAM Validation Guidelines. A two-step PCR first amplifies the target forensic STR and SNP loci (PCR1); unique, sample-specific indexed adapters or "barcodes" are attached in PCR2. Approximately 1736 ForenSeq™ reactions were analyzed. Studies include DNA substrate testing (cotton swabs, FTA cards, filter paper), species studies from a range of nonhuman organisms, DNA input sensitivity studies from 1ng down to 7.8pg, two-person human DNA mixture testing with three genotype combinations, stability analysis of partially degraded DNA, and effects of five commonly encountered PCR inhibitors. Calculations from ForenSeq™ STR and SNP repeatability and reproducibility studies (1ng template) indicate 100.0% accuracy of the MiSeq FGx™ System in allele calling relative to CE for STRs (1260 samples), and >99.1% accuracy relative to bead array typing for SNPs (1260 samples for iiSNPs, 310 samples for aiSNPs and piSNPs), with >99.0% and >97.8% precision, respectively. Call rates of >99.0% were observed for all STRs and SNPs amplified with both ForenSeq™ primer mixes. Limitations of the MiSeq FGx™ System are discussed. Results described here demonstrate that the MiSeq FGx™ System meets forensic DNA quality assurance guidelines with robust, reliable, and reproducible performance on samples of various quantities and qualities.

A memory efficient method for structure-based RNA multiple alignment.

Structure-based RNA multiple alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Existing tools for RNA multiple alignment first generate pairwise RNA structure alignments and then build the multiple alignment using only sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a structure-based RNA multiple alignment from one sequence with known structure and a database of sequences from the same family. PMFastR also has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm also provides a method to utilize a multicore environment. We present results on benchmark data sets from BRAliBase, which shows PMFastR performs comparably to other state-of-the-art programs. Finally, we regenerate 607 Rfam seed alignments and show that our automated process creates multiple alignments similar to the manually curated Rfam seed alignments. Thus, the techniques presented in this paper allow for the generation of multiple alignments using sequence-structure guidance, while limiting memory consumption. As a result, multiple alignments of long RNA sequences, such as 16S and 23S rRNAs, can easily be generated locally on a personal computer. The software and supplementary data are available at http://genome.ucf.edu/PMFastR.

AMASS: algorithm for MSI analysis by semi-supervised segmentation.

Mass Spectrometric Imaging (MSI) is a molecular imaging technique that allows the generation of 2D ion density maps for a large complement of the active molecules present in cells and sectioned tissues. Automatic segmentation of such maps according to patterns of co-expression of individual molecules can be used for discovery of novel molecular signatures (molecules that are specifically expressed in particular spatial regions). However, current segmentation techniques are biased toward the discovery of higher abundance molecules and large segments; they allow limited opportunity for user interaction, and validation is usually performed by similarity to known anatomical features. We describe here a novel method, AMASS (Algorithm for MSI Analysis by Semi-supervised Segmentation). AMASS relies on the discriminating power of a molecular signal instead of its intensity as a key feature, uses an internal consistency measure for validation, and allows significant user interaction and supervision as options. An automated segmentation of entire leech embryo data images resulted in segmentation domains congruent with many known organs, including heart, CNS ganglia, nephridia, nephridiopores, and lateral and ventral regions, each with a distinct molecular signature. Likewise, segmentation of a rat brain MSI slice data set yielded known brain features and provided interesting examples of co-expression between distinct brain regions. AMASS represents a new approach for the discovery of peptide masses with distinct spatial features of expression. Software source code and installation and usage guide are available at http://bix.ucsd.edu/AMASS/ .

Multiple changes in peptide and lipid expression associated with regeneration in the nervous system of the medicinal leech.

BACKGROUND: The adult medicinal leech central nervous system (CNS) is capable of regenerating specific synaptic circuitry after a mechanical lesion, displaying evidence of anatomical repair within a few days and functional recovery within a few weeks. In the present work, spatiotemporal changes in molecular distributions during this phenomenon are explored. Moreover, the hypothesis that neural regeneration involves some molecular factors initially employed during embryonic neural development is tested. RESULTS: Imaging mass spectrometry coupled to peptidomic and lipidomic methodologies allowed the selection of molecules whose spatiotemporal pattern of expression was of potential interest. The identification of peptides was aided by comparing MS/MS spectra obtained for the peptidome extracted from embryonic and adult tissues to leech transcriptome and genome databases. Through the parallel use of a classical lipidomic approach and secondary ion mass spectrometry, specific lipids, including cannabinoids, gangliosides and several other types, were detected in adult ganglia following mechanical damage to connected nerves. These observations motivated a search for possible effects of cannabinoids on neurite outgrowth. Exposing nervous tissues to Transient Receptor Potential Vanilloid (TRPV) receptor agonists resulted in enhanced neurite outgrowth from a cut nerve, while exposure to antagonists blocked such outgrowth. CONCLUSION: The experiments on the regenerating adult leech CNS reported here provide direct evidence of increased titers of proteins that are thought to play important roles in early stages of neural development. Our data further suggest that endocannabinoids also play key roles in CNS regeneration, mediated through the activation of leech TRPVs, as a thorough search of leech genome databases failed to reveal any leech orthologs of the mammalian cannabinoid receptors but revealed putative TRPVs. In sum, our observations identify a number of lipids and proteins that may contribute to different aspects of the complex phenomenon of leech nerve regeneration, establishing an important base for future functional assays.

Automated querying and identification of novel peptides using MALDI mass spectrometric imaging.

MSI is a molecular imaging technique that allows for the generation of topographic 2D maps for various endogenous and some exogenous molecules without prior specification of the molecule. In this paper, we start with the premise that a region of interest (ROI) is given to us based on preselected morphological criteria. Given an ROI, we develop a pipeline, first to determine mass values with distinct expression signatures, localized to the ROI, and second to identify the peptides corresponding to these mass values. To identify spatially differentiated masses, we implement a statistic that allows us to estimate, for each spectral peak, the probability that it is over- or under-expressed within the ROI versus outside. To identify peptides corresponding to these masses, we apply LC-MS/MS to fragment endogenous (nonprotease digested) peptides. A novel pipeline based on constructing sequence tags de novo from both original and decharged spectra and a subsequent database search is used to identify peptides. As the MSI signal and the identified peptide are only related by a single mass value, we isolate the corresponding transcript and perform a second validation via in situ hybridization of the transcript. We tested our approach, MSI-Query, on a number of ROIs in the medicinal leech, Hirudo medicinalis, including the central nervous system (CNS). The Hirudo CNS is capable of regenerating itself after injury, thus forming an important model system for neuropeptide identification. The pipeline helps identify a number of novel peptides. Specifically, we identify a gene that we name HmIF4, which is a member of the intermediate filament family involved in neural development and a second novel, uncharacterized peptide. A third peptide, derived from the histone H2B, is also identified, in agreement with the previously suggested role of histone H2B in axon targeting.

ChemDB: a public database of small molecules and related chemoinformatics resources.

MOTIVATION: The development of chemoinformatics has been hampered by the lack of large, publicly available, comprehensive repositories of molecules, in particular of small molecules. Small molecules play a fundamental role in organic chemistry and biology. They can be used as combinatorial building blocks for chemical synthesis, as molecular probes in chemical genomics and systems biology, and for the screening and discovery of new drugs and other useful compounds. RESULTS: We describe ChemDB, a public database of small molecules available on the Web. ChemDB is built using the digital catalogs of over a hundred vendors and other public sources and is annotated with information derived from these sources as well as from computational methods, such as predicted solubility and three-dimensional structure. It supports multiple molecular formats and is periodically updated, automatically whenever possible. The current version of the database contains approximately 4.1 million commercially available compounds and 8.2 million counting isomers. The database includes a user-friendly graphical interface, chemical reactions capabilities, as well as unique search capabilities. AVAILABILITY: Database and datasets are available on http://cdb.ics.uci.edu.

Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity.

MOTIVATION: Small molecules play a fundamental role in organic chemistry and biology. They can be used to probe biological systems and to discover new drugs and other useful compounds. As increasing numbers of large datasets of small molecules become available, it is necessary to develop computational methods that can deal with molecules of variable size and structure and predict their physical, chemical and biological properties. RESULTS: Here we develop several new classes of kernels for small molecules using their 1D, 2D and 3D representations. In 1D, we consider string kernels based on SMILES strings. In 2D, we introduce several similarity kernels based on conventional or generalized fingerprints. Generalized fingerprints are derived by counting in different ways subpaths contained in the graph of bonds, using depth-first searches. In 3D, we consider similarity measures between histograms of pairwise distances between atom classes. These kernels can be computed efficiently and are applied to problems of classification and prediction of mutagenicity, toxicity and anti-cancer activity on three publicly available datasets. The results derived using cross-validation methods are state-of-the-art. Tradeoffs between various kernels are briefly discussed. AVAILABILITY: Datasets available from http://www.igb.uci.edu/servers/servers.html