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Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq—a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

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.

Spurred by recent progress in medicinal chemistry, numerous lead compounds have sprung up in the past few years, although the majority are hindered by hydrophobicity, which greatly challenges druggability. In an effort to assess the potential of platinum (Pt) candidates, the nanosizing approach to alter the pharmacology of hydrophobic Pt(IV) prodrugs in discovery and development settings is described. The construction of a self-assembled nanoparticle (NP) platform, composed of amphiphilic lipid-polyethylene glycol (PEG) for effective delivery of Pt(IV) prodrugs capable of resisting thiol-mediated detoxification through a glutathione (GSH)-exhausting effect, offers a promising route to synergistically improving safety and efficacy. After a systematic screening, the optimized NPs (referred to as P6 NPs) exhibited small particle size (99.3 nm), high Pt loading (11.24%), reliable dynamic stability (∼7 days), and rapid redox-triggered release (∼80% in 3 days). Subsequent experiments on cells support the emergence of P6 NPs as a highly effective means of transporting a lethal dose of cargo across cytomembranes through macropinocytosis. Upon reduction by cytoplasmic reductants, particularly GSH, P6 NPs under disintegration released sufficient active Pt(II) metabolites, which covalently bound to target DNA and induced significant apoptosis. The PEGylation endowed P6 NPs with in vivo longevity and tumor specificity, which were essential to successfully inhibiting the growth of cisplatin-sensitive and -resistant xenograft tumors, while effectively alleviating toxic side-effects associated with cisplatin. P6 NPs are, therefore, promising for overcoming the bottleneck in the development of Pt drugs for oncotherapy.

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.

Metabolic diseases occur when normal metabolic processes are disrupted in the human body, which can be congenital or acquired. The incidence of metabolic diseases worldwide has reached epidemic proportions. So far, various methods including systemic drug therapy and surgery are exploited to prevent and treat metabolic diseases. However, current pharmacotherapeutic options for treatment of these metabolic disorders remain limited and ineffective, especially reducing patient compliance to treatment. Therefore, it is desirable to exploit effective drug delivery approaches to effectively treat metabolic diseases and reduce side effects. This brief review summarizes novel delivery strategies including local, targeted, and oral drug delivery strategies, as well as intelligent stimulus-responsive drug delivery strategy, for the treatment of metabolic disorders including diabetes, obesity, and atherosclerosis.

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 ide ntify 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.

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.