Biology subjects

Marc Salit

Publications and source records attributed to Marc Salit.

7 recordsLinked to original sources

bioRxiv2016

Genome-wide reconstruction of complex structural variants using read clouds

Recently developed methods that utilize partitioning of long genomic DNA fragments, and barcoding of shorter fragments derived from them, have succeeded in retaining long-range information in short sequencing reads. These so-called read cloud approaches represent a powerful, accurate, and cost-effective alternative to single-molecule long-read sequencing. We developed software, GROC-SVs, that takes advantage of read clouds for structural variant detection and assembly. We apply the method to two 10x Genomics data sets, one chromothriptic sarcoma with several spatially separated samples, and one breast cancer cell line, all Illumina-sequenced to high coverage. Comparison to short-fragment data from the same samples, and validation by mate-pair data from a subset of the sarcoma samples, demonstrate substantial improvement in specificity of breakpoint detection compared to short-fragment sequencing, at comparable sensitivity, and vice versa. The embedded longrange information also facilitates sequence assembly of a large fraction of the breakpoints; importantly, consecutive breakpoints that are closer than the average length of the input DNA molecules can be assembled together and their order and arrangement reconstructed, with some events exhibiting remarkable complexity. These features facilitated an analysis of the structural evolution of the sarcoma. In the chromothripsis, rearrangements occurred before copy number amplifications, and using the phylogenetic tree built from point mutation data we show that single nucleotide variants and structural variants are not correlated. We predict significant future advances in structural variant science using 10x data analyzed with GROC-SVs and other read cloud-specific methods.

Genomics↗

bioRxiv2016

Measurements of translation initiation from all 64 codons in E. coli

Our understanding of translation is one cornerstone of molecular biology that underpins our capacity to engineer living matter. The canonical start codon (AUG) and a few near-cognates (GUG, UUG) are typically considered as the \"start codons\" for translation initiation in Escherichia coli (E. coli). Translation is typically not thought to initiate from the 61 remaining codons. Here, we systematically quantified translation initiation in E. coli from all 64 triplet codons. We detected protein synthesis above background initiating from at least 46 codons. Translation initiated from these non-canonical start codons at levels ranging from 0.01% to 2% relative to AUG. Translation initiation from non-canonical start codons may contribute to the synthesis of peptides in both natural and synthetic biological systems

Synthetic Biology↗

bioRxiv2016

Characterizing the Non-Normal Distribution of Flow Cytometry Measurements from Transiently Expressed Constructs in Mammalian Cells

In mammalian cells, transient gene expression (TGE) is a rapid, minimal-investment alternative to single-copy integrations for testing of transgenic constructs. However, transient gene expression, as measured by flow cytometry with a fluorescent reporter, typically displays a broad, asymmetric distribution with a left-tail that is convolved with background signal. Common approaches for deriving a summary statistic for transiently expressed gene products impose a normal distribution on gated or ungated data. Summary statistics derived from these models are heavily biased by experimental conditions and instrument settings that are difficult to replicate and insufficient to accurately describe the underlying data. Here, we present a convolved gamma distribution as a superior model for TGE datasets. The 4-6 parameters of this model are sufficient to accurately describe the entire, ungated distribution of transiently transfected HEK cells expressing monomeric fluorescent proteins, that operates consistently across a range of transfection conditions and instrument settings. Based on these observations, a convolved gamma model of TGE distributions has the potential to significantly improve the accuracy and reproducibility of genetic device characterization in mammalian cells.

Synthetic Biology↗

bioRxiv2016

When Wavelengths Collide: Bias in Cell Abundance Measurements due to Expressed Fluorescent Proteins

The abundance of bacteria in liquid culture is commonly inferred by measuring optical density at 600 nm. Red fluorescent proteins (RFP) can strongly absorb light at 600 nm. Increasing RFP expression can falsely inflate apparent cell density and lead to underestimations of mean per-cell fluorescence by up to 10%. Measuring optical density at 700 nm would allow estimation of cell abundance unaffected by the presence of nearly all fluorescent proteins.

Synthetic Biology↗

bioRxiv2016

Evaluation of the External RNA Controls Consortium (ERCC) reference material using a modified Latin square design.

BACKGROUNDHighly multiplexed assays for quantitation of RNA transcripts are being used in many areas of biology and medicine. Using data generated by these transcriptomic assays requires measurement assurance with appropriate controls. Methods to prototype and evaluate multiple RNA controls were developed as part of the External RNA Controls Consortium (ERCC) assessment process. These approaches included a modified Latin square design to provide a broad dynamic range of relative abundance with known differences between four complex pools of ERCC RNA transcripts spiked into a human liver total RNA background.\n\nRESULTSERCC pools were analyzed on four different microarray platforms: Agilent 1- and 2-color, Illumina bead, and NIAID lab-made spotted microarrays; and two different second-generation sequencing platforms: the Life Technologies 5500xl and the Illumina HiSeq 2500. Individual ERCCs were assessed for reproducible performance in signal response to concentration among the platforms. Most demonstrated linear behavior if they were not located near one of the extremes of the dynamic range. Performance issues with any individual ERCC transcript could be attributed to detection limitations, platform-specific target probe issues, or potential mixing errors. Collectively, these pools of spike-in RNA controls were evaluated for suitability as surrogates for endogenous transcripts to interrogate the performance of the RNA measurement process of each platform. The controls were useful for establishing the dynamic range of the assay, as well as delineating the useable region of that range where differential expression measurements, expressed as ratios, would be expected to be accurate.\n\nCONCLUSIONSThe modified Latin square design presented here uses a composite testing scheme for the evaluation of multiple performance characteristics: linear performance of individual controls, signal response within dynamic range pools of controls, and ratio detection between pairs of dynamic range pools. This compact design provides an economical sample format for the evaluation of multiple external RNA controls within a single experiment per platform. These results indicate that well-designed pools of RNA controls, spiked-into samples, provide measurement assurance for endogenous gene expression experiments.

Genomics↗

bioRxiv2015

svviz: a read viewer for validating structural variants

Visualizing read alignments is the most effective way to validate candidate SVs with existing data. We present svviz, a sequencing read visualizer for structural variants (SVs) that sorts and displays only reads relevant to a candidate SV. svviz works by searching input bam(s) for potentially relevant reads, realigning them against the inferred sequence of the putative variant allele as well as the reference allele, and identifying reads that match one allele better than the other. Reads are assigned to the proper allele based on alignment score, read pair orientation and insert size. Separate views of the two alleles are then displayed in a scrollable web browser view, enabling a more intuitive visualization of each allele, compared to the single reference genome-based view common to most current read browsers. The web view facilitates examining the evidence for or against a putative variant, estimating zygosity, visualizing affected genomic annotations, and manual refinement of breakpoints. An optional command-line-only interface allows summary statistics and graphics to be exported directly to standard graphics file formats. svviz is open source and freely available from github, and requires as input only structural variant coordinates (called using any other software package), reads in bam format, and a reference genome. Reads from any high-throughput sequencing platform are supported, including Illumina short-read, mate-pair, synthetic long-read (assembled), Pacific Biosciences, and Oxford Nanopore. svviz is open source and freely available from https://github.com/svviz/svviz.

Genomics↗

bioRxiv2015

Using Mixtures of Biological Samples as Process Controls for RNA-sequencing experiments

BackgroundGenome-scale \"-omics\" measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls.\n\nResultsWe apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the messenger RNA (mRNA) content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine mRNA content of total RNA in samples.\n\nConclusionsGenome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The mRNA fraction accounts for differential enrichment of mRNA from varying total RNA samples. Spike-in controls can be utilized to measure this relationship between mRNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.

Bioinformatics↗