Biology subjects

Salzman, J.

Publications and source records attributed to Salzman, J..

27 records · Page 2Linked to original sources

bioRxiv2021

RNA splicing programs define tissue compartments and cell types at single cell resolution

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

bioinformatics↗

bioRxiv2020

The SZS is an efficient statistical method to identify regulated splicing events in droplet-based RNA sequencing

To date, detecting robust single-cell-regulated splicing is viewed as out of reach from droplet based technologies such as 10x Chromium. This prevents the discovery of single-cell-regulated splicing in rare cell types or those that are difficult or impossible to sequence deeply. Here, we introduce a novel, robust, and computationally efficient set of statistics, the Splicing Z Score (SpliZ) and SpliZVD, to detect regulated splicing in single cell RNA-seq including 10x Chromium. The SpliZ(VD) provides annotation-free detection of differentially regulated, complex alternative splicing events. The SpliZ generalizes and increases statistical power compared to the Percent Spliced In (PSI) and mathematically reduces to PSI for simple exon-skipping. We applied the SpliZ to primary human lung cells to discover hundreds of genes with new regulated cell-type-specific splicing. The SpliZ has wide application to enable biological discovery of genes predicted to have functionally significant splicing programs including those regulated in development.

bioinformatics↗

bioRxiv2020

TGS1 controls snRNA 3' end processing, prevents neurodegeneration and ameliorates SMN-dependent neurological phenotypes in vivo

Trimethylguanosine synthase 1 (TGS1) is a highly conserved enzyme that converts the 5 mono-methylguanosine cap of snRNAs to a trimethylguanosine cap. Here, we show that loss of TGS1 in C. elegans, D. melanogaster and D. rerio results in neurological phenotypes similar to those caused by Survival Motor Neuron (SMN) deficiency. Importantly, expression of human TGS1 ameliorates the SMN-dependent neurological phenotypes in both flies and worms, revealing that TGS1 can partly counteract the effects of SMN deficiency. TGS1 loss in HeLa cells leads to the accumulation of immature U2 and U4atac snRNAs with long 3 tails that are often uridylated. snRNAs with defective 3 terminations also accumulate in Drosophila Tgs1 mutants. Consistent with defective snRNA maturation, TGS1 and SMN mutant cells also exhibit partially overlapping transcriptome alterations that include aberrantly spliced and readthrough transcripts. Together, these results identify a neuroprotective function for TGS1 and reinforce the view that defective snRNA maturation affects neuronal viability and function.

genetics↗

bioRxiv2020

Specific splice junction detection in single cells with SICILIAN

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIANs precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, increases agreement between biological replicates, and reliably detects un-annotated splicing in single cells, enabling the discovery of novel splicing regulation.

bioinformatics↗

bioRxiv2019

Improved detection of gene fusions by applying statistical methods reveals new oncogenic RNA cancer drivers

Short AbstractThe extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce a new algorithm, DEEPEST, that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling ten-fold fewer false-positive fusions in non-transformed human tissues. We leverage the increased precision of DEEPEST to discover new cancer biology. For example, 888 new candidate oncogenes are identified based on over-representation in DEEPEST-Fusion calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs partners, demonstrating a previously unappreciated prevalence and potential for function. Specific protein domains are enriched in DEEPEST calls, demonstrating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. DEEPEST also reveals a high enrichment for fusions involving known and novel oncogenes in diseases including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.\n\nSignificanceGene fusions are tumor-specific genomic aberrations and are among the most powerful biomarkers and drug targets in translational cancer biology. The advent of RNA-Seq technologies over the past decade has provided a unique opportunity for detecting novel fusions via deploying computational algorithms on public sequencing databases. Yet, precise fusion detection algorithms are still out of reach. We develop DEEPEST, a highly specific and efficient statistical pipeline specially designed for mining massive sequencing databases, and apply it to all 33 tumor types and 10,500 samples in The Cancer Genome Atlas database. We systematically profile the landscape of detected fusions via employing classic statistical models and identify several signatures of selection for fusions in tumors.\n\nSoftware availabilityDEEPEST-Fusion workflow with a detailed readme file is available as a Github repository: https://github.com/salzmanlab/DEEPEST-Fusion. In addition to the main workflow, which is based on CWL, example input and batch scripts (for job submission on local clusters), and codes for building the SBT files and SBT querying are provided in the repository. All custom scripts used for systematic analysis of fusions are also available in the same repository.

bioinformatics↗

bioRxiv2018

Logarithmic molecular sampling for next-generation sequencing

Next-generation sequencing enables measurement of chemical and biological signals at high throughput and falling cost. Conventional sequencing requires increasing sampling depth to improve signal to noise discrimination, a costly procedure that is also impossible when biological material is limiting. We introduce a new general sampling theory, Molecular Entropy encodinG (MEG), which uses biophysical principles to functionally encode molecular abundance before sampling. SeQUential DepletIon and enriCHment (SQUICH) is a specific example of MEG that, in theory and simulation, enables sampling at a logarithmic or better rate to achieve the same precision as attained with conventional sequencing. In proof-of-principle experiments, SQUICH reduces sequencing depth by a factor of 10. MEG is a general solution to a fundamental problem in molecular sampling and enables a new generation of efficient, precise molecular measurement at logarithmic or better sampling depth.

genomics↗

bioRxiv2018

Discovery of gene regulatory elements through a new bioinformatics analysis of haploid genetic screens

The systematic identification of regulatory elements that control gene expression remains a challenge. Genetic screens that use untargeted mutagenesis have the potential to identify protein-coding genes, non-coding RNAs and regulatory elements, but their analysis has mainly focused on identifying the former two. To identify regulatory elements, we conducted a new bioinformatics analysis of insertional mutagenesis screens interrogating WNT signaling in haploid human cells. We searched for specific patterns of retroviral gene trap integrations (used as mutagens in haploid screens) in short genomic intervals overlapping with introns and regions upstream of genes. We uncovered atypical patterns of gene trap insertions that were not predicted to disrupt coding sequences, but caused changes in the expression of two key regulators of WNT signaling, suggesting the presence of cis-regulatory elements. Our methodology extends the scope of haploid genetic screens by enabling the identification of regulatory elements that control gene expression.

bioinformatics↗

bioRxiv2017

Precise, pan-cancer discovery of gene fusions reveals a signature of selection in primary tumors

Short AbstractThe extent to which gene fusions function as drivers of cancer remains a critical open question in cancer biology. In principle, transcriptome sequencing provided by The Cancer Genome Atlas (TCGA) enables unbiased discovery of gene fusions and post-analysis that informs the answer to this question. To date, such an analysis has been impossible because of performance limitations in fusion detection algorithms. By engineering a new, more precise, algorithm and statistical approaches to post-analysis of fusions called in TCGA data, we report new recurrent gene fusions, including those that could be druggable; new candidate pan-cancer oncogenes based on their profiles in fusions; and prevalent, previously overlooked, candidate oncogenic gene fusions in ovarian cancer, a disease with minimal treatment advances in recent decades. The novel and reproducible statistical algorithms and, more importantly, the biological conclusions open the door for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.

cancer biology↗

bioRxiv2017

ciRS-7 exonic sequence is embedded in a long-noncoding RNA locus

ciRS-7 is an intensely studied, highly expressed and conserved circRNA. Essentially nothing is known about its biogenesis, including the location of its promoter. A prevailing assumption has been that ciRS-7 is an exceptional circRNA because it is transcribed from a locus lacking any mature linear RNA transcripts of the same sense. Our interest in the biogenesis of ciRS-7 led us to develop an algorithm to define its promoter. This approach predicted that the human ciRS-7 promoter coincides with that of the long non-coding RNA, LINC00632. We validated this prediction using multiple orthogonal experimental assays. We also used computational approaches and experimental validation to establish that ciRS-7 exonic sequence is embedded in linear transcripts that are flanked by cryptic exons in both human and mouse. Together, this experimental and computational evidence generate a new view of regulation in this locus: (a) ciRS-7 is like other circRNAs, as it is spliced into linear transcripts; (b) expression of ciRS-7 is primarily determined by the chromatin state of LINC00632 promoters; (c) transcription and splicing factors sufficient for ciRS-7 biogenesis are expressed in cells that lack detectable ciRS-7 expression. These findings have significant implications for the study of the regulation and function of ciRS-7, and the analytic framework we developed to jointly analyze RNA-seq and ChlP-seq data reveal the potential for genome-wide discovery of important biological regulation missed in current reference annotations.\n\nAuthor SummarycircRNAs were recently discovered to be a significant product of host gene expression programs but little is known about their transcriptional regulation. Here, we have studied the expression of a well-known circRNA named ciRS-7. ciRS-7 has an unusual function for a circRNA; it is believed to be a miRNA sponge. Previously, ciRS-7 was thought to be transcribed from a locus lacking any mature linear isoforms, unlike all other circular RNAs known to be expressed in human cells. However, we have found this to be false; using a combination of bioinformatic and experimental genetic approaches, in both human and mouse, we discovered that linear transcripts containing the ciRS-7 exonic sequence, linking it to upstream genes. This suggests the potential for additional functional roles of this important locus and provides critical information to begin study on the biogenesis of ciRS-7.

genetics↗