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The scope and scale of biological data are increasing at an exponential rate, as technologies like next-generation sequencing are becoming radically cheaper and more prevalent. Over the last two decades, the cost of sequencing a genome has dropped from $100 million to nearly $100—a factor of over 10 6 —and the amount of data to be analyzed has increased proportionally. Yet, as Moore’s Law continues to slow, computational biologists can no longer rely on computing hardware to compensate for the ever-increasing size of biological datasets. In a field where many researchers are primarily focused on biological analysis over computational optimization, the unfortunate solution to this problem is often to simply buy larger and faster machines. Here, we introduce Seq , the first language tailored specifically to bioinformatics, which marries the ease and productivity of Python with C-like performance. Seq starts with a subset of Python—and is in many cases a drop-in replacement—yet also incorporates novel bioinformatics- and computational genomics-oriented data types, language constructs and optimizations. Seq enables users to write high-level, Pythonic code without having to worry about low-level or domain-specific optimizations, and allows for the seamless expression of the algorithms, idioms and patterns found in many genomics or bioinformatics applications. We evaluated Seq on several standard computational genomics tasks like reverse complementation, k -mer manipulation, sequence pattern matching and large genomic index queries. On equivalent CPython code, Seq attains a performance improvement of up to two orders of magnitude, and a 160× improvement once domain-specific language features and optimizations are used. With parallelism, we demonstrate up to a 650× improvement. Compared to optimized C++ code, which is already difficult for most biologists to produce, Seq frequently attains up to a 2× improvement, and with shorter, cleaner code. Thus, Seq opens the door to an age of democratization of highly-optimized bioinformatics software.

Loss of function in tumor suppressor genes is commonly associated with the onset/progression of cancer and treatment resistance. The p53 tumor suppressor gene, a master regulator of diverse cellular pathways, is frequently altered in various cancers, for example, in ~36% of hepatocellular carcinomas (HCCs) and ~68% of non-small cell lung cancers (NSCLCs). Current methods for restoration of p53 expression, including small molecules and DNA therapies, have yielded progressive success, but each has formidable drawbacks. Here, a redox-responsive nanoparticle (NP) platform is engineered for effective delivery of p53-encoding synthetic messenger RNA (mRNA). We demonstrate that the synthetic p53-mRNA NPs markedly delay the growth of p53-null HCC and NSCLC cells by inducing cell cycle arrest and apoptosis. We also reveal that p53 restoration markedly improves the sensitivity of these tumor cells to everolimus, a mammalian target of rapamycin (mTOR) inhibitor that failed to show clinical benefits in advanced HCC and NSCLC. Moreover, cotargeting of tumor-suppressing p53 and tumorigenic mTOR signaling pathways results in marked antitumor effects in vitro and in multiple animal models of HCC and NSCLC. Our findings indicate that restoration of tumor suppressors by the synthetic mRNA NP delivery strategy could be combined together with other therapies for potent combinatorial cancer treatment.

Recent years have seen the emergence of several “third-generation” sequencing platforms, each of which aims to address shortcomings of standard next-generation short-read sequencing by producing data that capture long-range information, thereby allowing us to access regions of the genome that are inaccessible with short-reads alone. These technologies either produce physically longer reads typically with higher error rates or instead capture long-range information at low error rates by virtue of read “barcodes” as in 10x Genomics’ Chromium platform. As with virtually all sequencing data, sequence alignment for third-generation sequencing data is the foundation on which all downstream analyses are based. Here we introduce a latent variable model for improving barcoded read alignment, thereby enabling improved downstream genotyping and phasing. We demonstrate the feasibility of this approach through developing EMerAld— or EMA for short— and testing it on the barcoded short-reads produced by 10x’s sequencing technologies. EMA not only produces more accurate alignments, but unlike other methods also assigns interpretable probabilities to the alignments it generates. We show that genotypes called from EMA’s alignments contain over 30% fewer false positives than those called from Lariat’s (the current 10x alignment tool), with a fewer number of false negatives, on datasets of NA12878 and NA24385 as compared to NIST GIAB gold standard variant calls. Moreover, we demonstrate that EMA is able to effectively resolve alignments in regions containing nearby homologous elements— a particularly challenging problem in read mapping— through the introduction of a novel statistical binning optimization framework, which allows us to find variants in the pharmacogenomically important CYP2D region that go undetected when using Lariat or BWA. Lastly, we show that EMA’s alignments improve phasing performance compared to Lariat’s in both NA12878 and NA24385, producing fewer switch/mismatch errors and larger phase blocks on average.EMA software and datasets used are available at http://ema.csail.mit.edu.

Motivation As the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS). Results We introduce lightweight assignment of variant alleles (LAVA), an NGS-based genotyping algorithm for a given set of SNP loci, which takes advantage of the fact that approximate matching of mid-size k-mers (with k = 32) can typically uniquely identify loci in the human genome without full read alignment. LAVA accurately calls the vast majority of SNPs in dbSNP and Affymetrix’s Genome-Wide Human SNP Array 6.0 up to about an order of magnitude faster than standard NGS genotyping pipelines. For Affymetrix SNPs, LAVA has significantly higher SNP calling accuracy than existing pipelines while using as low as ∼5 GB of RAM. As such, LAVA represents a scalable computational method for population-level genotyping studies as well as a flexible NGS-based replacement for SNP arrays. Availability and Implementation LAVA software is available at http://lava.csail.mit.edu. Contact bab@mit.edu Supplementary information Supplementary data are available at Bioinformatics online.

Sequencing technologies are capturing longer-range genomic information at lower error rates, enabling alignment to genomic regions that are inaccessible with short reads. However, many methods are unable to align reads to much of the genome, recognized as important in disease, and thus report erroneous results in downstream analyses. We introduce EMA, a novel two-tiered statistical binning model for barcoded read alignment, that first probabilistically maps reads to potentially multiple “read clouds” and then within clouds by newly exploiting the non-uniform read densities characteristic of barcoded read sequencing. EMA substantially improves downstream accuracy over existing methods, including phasing and genotyping on 10x data, with fewer false variant calls in nearly half the time. EMA effectively resolves particularly challenging alignments in genomic regions that contain nearby homologous elements, uncovering variants in the pharmacogenomically important CYP2D region, and clinically important genes C4 (schizophrenia) and AMY1A (obesity), which go undetected by existing methods. Our work provides a framework for future generation sequencing. Researchers are applying barcoded read sequencing to capture longer-range information in the genome at low error rates. We introduce a two-tiered statistical binning model, named EMA, which probabilistically assigns reads to “clouds” and then optimizes read assignments within clouds based on read densities. Unlike previous approaches, our efficient method enables alignment to highly homologous regions of the genome important in disease and substantially improves downstream genotyping and haplotyping. Our method also uncovers rare variants in clinically important genes. Keywords: third-generation sequencing; read mapping; barcoded short-reads; linked-reads, National Institutes of Health (U.S.) (Grant GM108348)

This article reviews the principal modes of dental composite material degradation and relates them to the specific components of the composites themselves. Particular emphasis is placed on the selection of the monomer resins, the filler content, and the degree of monomer conversion after the clinical materials are cured. Loss of mechanical function and leaching of components from the composites are briefly described, while a more detailed description is provided of studies that have considered the chemical breakdown of materials by agents that are present in the oral cavity, or model the latter. Specific attention will be given to the hydrolysis process of monomer and composite components, i.e., the scission of condensation-type bonds (esters, ethers, amides, etc.) that make up the monomer resins, following reaction of the resins with water and salivary enzymes. A synopsis of enzyme types and their sources is outlined, along with a description of the work that supports their ability to attack and degrade specific types of monomer systems. The methods for the study of biodegradation effects are compared in terms of sensitivity and the information that they provide. The impact of biodegradation on the ultimate biocompatibility of current materials is discussed from the perspective of what is known to date and what remains to be studied. The findings of the past decade clearly indicate that there are many reasons to probe the issue of biochemical stability of composite resins in the oral cavity. The challenge will now be to have both industry and government agencies take a pro-active approach to fund research in this area, with the expectation that these studies will lead to a more concise definition of biocompatibility issues related to dental composites. In addition, the acquired information from such studies will generate the development of alternate polymeric chemistries and composite formulations that will require further investigation for use as the next generation of restorative materials with enhanced biostability.

The research literature suggests that current dental polymeric composites are not chemically inert at the material/biological interface. Several studies have investigated the process of "biodegradation" on dental composites in the presence of enzymes, by monitoring changes in weight loss and surface hardness properties. However, it is hypothesized that these methods can provide an erroneous measure of biochemically induced degradation, since they are less sensitive to molecular events and lack the ability to provide chemical information. Knowledge of the latter is important because it relates to the biological significance of biodegradation, i.e., the identification and quantification of released compounds that may be capable of influencing cell, bacteria, or enzyme function. It was the objective of this study to compare three methods (weight loss, surface micro-hardness, and liquid chromatography combined with mass spectrometry) for their ability to measure the effect of enzyme-induced biodegradation on three commercial composite resin materials. The enzyme was cholesterol esterase, and the composites were Silux Plus XL, Z100 A2 (3M), and TPH XL (L.D. Caulk). Biodegradation was readily detected by liquid chromatography, and its sensitivity was shown to be substantially greater than that of weight loss or surface hardness measurements, although surface hardness measurements did show some agreement with liquid chromatography data. The data also indicated that the levels and distribution of released degradation products can vary substantially from one product to the next, and that this merits further investigation if the potential impact of different commercial restorative materials on cell and bacteria function is to be assessed.