Biology subjects

Shao, M.

Publications and source records attributed to Shao, M..

54 records · Page 3Linked to original sources

bioRxiv2021

Scallop2 enables accurate assembly of multiple-end RNA-seq data

Transcript assembly (i.e., to reconstruct the full-length expressed transcripts from RNA-seq data) has been a critical but yet unsolved step in RNA-seq analysis. Modern RNA-seq protocols can produce paired-/multiple-end RNA-seq reads, where information is available that two or more reads originate from the same transcript. The long-range constraints implied in these paired-/multiple-end reads can be much beneficial in correctly phasing the complicated spliced isoforms. However, there often exist gaps among individual ends, which may even contain junctions, making the efficient use of such constraints algorithmically challenging. Here we introduce Scallop2, a new reference-based transcript assembler optimized for multiple-end (including paired-end) RNA-seq data. Scallop2 uses an algorithmic frame-work that first represents reads from the same molecule as the so-called multiple-end phasing paths in the context of a splice graph, then "bridges" each multiple-end phasing path into a long, single-end phasing path, and finally decomposes the splice graph into paths (i.e., transcripts) guided by the bridged phasing paths. An efficient bridging algorithm is designed to infer the true path connecting two consecutive ends following a novel formulation that is robust to sequencing errors and transcript noises. By observing that failing to bridge two ends is mainly due to incomplete splice graphs, we propose a new method to determine false starting/ending vertices of the splice graphs which has been showed efficient in reducing false positive rate. Evaluations on both (multiple-end) single-cell RNA-seq datasets from Smart-seq3 protocol and Illumina paired-end RNA-seq samples demonstrate that Scallop2 vastly outperforms recent assemblers including StringTie2, Scallop, and CLASS2 in assembly accuracy.

bioinformatics↗

bioRxiv2021

Two microRNA regulatory circuits set start and end times for dendritic arborization of a nociceptive neuron

Choreographic dendritic arborization takes place within a defined time frame, but the timing mechanism is currently not known. Here, we report that the precisely timed lin-4-lin-14 regulatory circuit triggers an initial dendritic growth activity whereas the precisely timed lin-28-let-7-lin-41 regulatory circuit signals a subsequent developmental decline in dendritic growth ability, hence restricting dendritic arborization within a set time frame. Loss-of-function mutations in the lin-4 microRNA gene cause limited dendritic outgrowth whereas loss-of-function mutations in its direct target, the lin-14 transcription factor gene, cause precocious and excessive outgrowth. In contrast, loss-of-function mutations in the let-7 microRNA gene prevent a developmental decline in dendritic growth ability whereas loss-of-function mutations in its upstream regulator, the lin-28 RNA-binding protein gene, and its direct target, the lin-41 tripartite motif protein gene, cause further decline. lin-4 and let-7 regulatory circuits are expressed in the right place at the right time to set start and end times for PVD dendritic arborization. Replacing the endogenous lin-4 promoter at the lin-4 locus with a late-onset let-7 promoter delays PVD dendrite arborization whereas replacing the endogenous let-7 promoter at the let-7 locus with an early-onset lin-4 promoter causes precocious decline in dendritic growth ability in PVD neurons. Our results indicate that the lin-4-lin-14 and the lin-28-let-7-lin-41 regulatory circuits control the timing of dendrite arborization through antagonistic regulation of the DMA-1 receptor level on dendrites. The LIN-14 transcription factor directly represses the dma-1 gene expression through a transcriptional means whereas the LIN-41 tripartite motif protein indirectly activates the dma-1 gene expression through a post-transcriptional means.

neuroscience↗

bioRxiv2021

GSA: An Independent Development Algorithm for Calling Copy Number and Detecting Homologous Recombination Deficiency (HRD) from Target Capture Sequencing

BackgroundThe gain or loss of large chromosomal regions or even whole chromosomes is termed as genomic scarring and can be observed as copy number variations resulting from the failure of DNA damage repair. ResultsIn this study, a new algorithm called Genomic Scar Analysis (GSA) has developed and validated to calculate homologous recombination deficiency (HRD) score. The two critical submodules were tree recursion (TR) segmentation and filtering, and the estimation and correction of the tumor purity and ploidy. Then, this study evaluated the rationality of segmentation and genotype identification by the GSA algorithm and compared with other two algorithms, PureCN and ASCAT, found that the segmentation result of GSA algorithm was more logical. In addition, the results indicated that the GSA algorithm had an excellent predictive effect on tumor purity and ploidy, if the tumor purity was more than 20%. Furtherly, this study evaluated the HRD scores and BRCA1/2 deficiency status of 195 clinical samples, and the results indicated that the accuracy was 0.98 (comparing with Affymetrix OncoScan assay) and the sensitivity was 95.2% (comparing with BRCA1/2 deficiency status), both were well-behaved. Finally, HRD scores and 16 genes mutations (TP53 and 15 HRR pathway genes) were analyzed in 17 cell lines, the results showed that there was higher frequency in HRR pathway genes in high HRD score samples. ConclusionsThis new algorithm, named as GSA, could effectively and accurately calculate the purity and ploidy of tumor samples through NGS data, and then reflect the degree of genomic instability and large-scale copy number variations of tumor samples.

bioinformatics↗

bioRxiv2021

Impaired developmental microglial pruning of excitatory synapses on CRH-expressing hypothalamic neurons exacerbates stress responses throughout life

The developmental origins of stress-related mental illnesses are well-established, and early-life stress/adversity (ELA) is an important risk factor. However, it is unclear how ELA impacts the maturation of salient brain circuits, provoking enduring vulnerability to stress and stress-related disorders. Here we find that ELA increases the number and function of excitatory synapses onto stress-sensitive hypothalamic corticotropin-releasing hormone (CRH)-expressing neurons, and implicate disrupted synapse pruning by microglia as a key mechanism. Microglial process dynamics on live imaging, and engulfment of synaptic elements by microglia, were both attenuated in ELA mice, associated with deficient signaling of the microglial phagocytic receptor Mer. Accordingly, selective chemogenetic activation of ELA microglia increased microglial process dynamics and reduced excitatory synapse density to control levels. Selective early-life microglial activation also mitigated the adrenal hypertrophy and prolonged stress responses in adult ELA mice, establishing microglial actions during development as powerful contributors to experience-dependent sculpting of stress-related brain circuits.

neuroscience↗

bioRxiv2021

Whole Genome Assembly and Annotation of Northern Wild Rice, Zizania palustris L., Supports a Whole Genome Duplication in the Zizania Genus

Northern Wild Rice (NWR; Zizania palustris L.) is an aquatic grass native to North America that is notable for its nutritious grain. This is an important species with ecological, cultural, and agricultural significance, specifically in the Great Lakes region of the United States. Using long- and short-range sequencing, Hi-C scaffolding, and RNA-seq data from eight tissues, we generated an annotated whole genome de novo assembly of NWR. The assembly is 1.29 Gb, highly repetitive ([~]76.0%), and contains 46,421 putative protein-coding genes. The expansion of retrotransposons within the genome and a whole genome duplication prior to the Zizania-Oryza speciation event have both led to an increase in genome size of NWR in comparison with O. sativa and Z. latifolia. Both events depict a genome rapidly undergoing change over a short evolutionary time. Comparative analyses revealed conservation of large syntenic blocks with Oryza sativa L., which were used to identify putative seed shattering genes. Estimates of divergence times revealed the Zizania genus diverged from Oryza [~]26-30 million years ago (MYA), while NWR and Zizania latifolia diverged from one another [~]6-8 MYA. Comparative genomics confirmed evidence of a whole genome duplication in the Zizania genus and provided support that the event was prior to the NWR-Z. latifolia speciation event. This high-quality genome assembly and annotation provides a valuable resource for comparative genomics in the Oryzeae tribe and provides an important resource for future conservation and breeding efforts of NWR.

genomics↗

bioRxiv2021

On bridging paired-end RNA-seq data

MotivationThe widely-used high-throughput RNA-sequencing technologies (RNA-seq) usually produce paired-end reads. We explore if full fragments can be computationally reconstructed from the sequenced two ends--a problem here we refer to as bridging. Solving this problem provides longer, more informative RNA-seq reads, and hence benefits downstream RNA-seq analysis such as transcriptome assembly and expression quantification. However, bridging is a challenging and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data itself provides sufficient information for accurate bridging, let alone proper models and efficient algorithms that characterize and determine the true bridges. Algorithmic ResultsWe studied this problem in two settings: reference-based bridging, which assumes reads alignments are available and reconstructs the alignments of full fragments, and de novo bridging, which reconstructs sequences of entire fragments from sequences of the two ends. We proposed a novel mathematical formulation that works for both settings--to seek a path in an underlying graph data structure (i.e., splice graph for reference-based bridging, and compacted de Bruijn graph for de novo bridging) such that its bottleneck weight is maximized. This formulation characterizes true bridges and is efficient in filtering out false bridges. This formulation admits optimal substructure property, and hence efficient dynamic programming algorithms can be designed. For reference-based bridging, we designed such an algorithm to calculate the top N bridging paths, followed by a voting approach to select one using the distribution of fragment length. For de novo bridging, we designed a new truncated Dijkstras algorithm. To further speed up, we proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Dijkstras algorithm from scratch for all vertices. These innovations result in scalable algorithms that can bridge all paired-end reads in a compacted de Bruijn graph with millions of vertices. Experimental ResultsWe showed that paired-end RNA-seq reads can be accurately bridged to a large extend. Our reference-based bridging tool could correctly bridge more than 79.6% of reads. For de novo bridging, high precision was observed with varied sensitivity. We also showed that bridging can improve reference-based transcript assembly: the improvement was significant (up to 14.4% measured with adjusted precision), and universal in all combinations with different aligners and assemblers. AvailabilityImplementations of the algorithms for reference-based and de novo bridging are available at https://github.com/Shao-Group/rnabridge-align and https://github.com/Shao-Group/rnabridge-denovo, respectively. Scripts, datasets, and documentations that can reproduce the experimental results in this manuscript are available at https://github.com/Shao-Group/rnabridge-test.

bioinformatics↗

bioRxiv2020

Coral accurately bridges paired-end RNA-seq reads alignment

MotivationThe established high-throughput RNA-seq technologies usually produce paired-end reads. A challenging problem is therefore to computationally infer the alignment of entire fragments given the alignment of the two mate ends. Solving this problem essentially provide longer RNA-seq reads, and hence benefits downstream RNA-seq analysis. ResultsWe introduce Coral, a new tool that can accurately bridge paired-end RNA-seq reads. The core of Coral is a novel optimization formulation that can capture the most reliable bridging path while also filter out false paths. An efficient dynamic programming algorithm is designed to calculate the top N optimum. Coral implements a consensus approach to select the best solution among the N candidates by taking into account the distribution of fragment length. Coral is modular, can be easily incorporated into existing RNA-seq analysis pipeline. We show that Coral can improve transcript assembly by a large margin: on average over 2377 RNA-seq samples from GTEx, the improvement (measured with adjusted precision) is 7.5% and 11.2% when Coral is incorporated with StringTie and Scallop, respectively. AvailabilityCoral is open-source, freely available at GitHub (https://github.com/Shao-Group/coral) and Bioconda. Scripts, datasets and documentations that can reproduce all experimental results in this paper are available at https://github.com/Shao-Group/coraltest.

bioinformatics↗

bioRxiv2019

Genome-wide variations analysis of special waxy sorghum cultivar Hongyingzi for brewing Moutai liquor

AbstractsHongyingzi is a special waxy sorghum (Sorghum bicolor L. Moench) cultivar for brewing Moutai liquor. For an overall understanding of the whole genome of Hongyingzi, we performed whole-genome resequencing technology with 56.10 X depth to reveal its comprehensive variations. Compared with the BTx623 reference genome, 2.48% of genome sequences were altered in the Hongyingzi genome. Among these alterations, there were 1885774 single nucleotide polymorphisms (SNPs), 309381 small fragments insertions and deletions (Indels), 31966 structural variations (SVs), and 217273 copy number variations (CNVs). These alterations conferred 29614 genes variations. It was also predicted that 35 genes variations were related to the multidrug and toxic efflux (MATE) transporter, chalcone synthase (CHS), ATPase isoform 10 (AHA10) transporter, dihydroflavonol-4-reductase (DFR), the laccase 15 (LAC15), flavonol 3'-hydroxylase (F3'H), flavanone 3-hydroxylase (F3H), O-methyltransferase (OMT), flavonoid 3'5' hydroxylase (F3'5'H), UDP-glucose:sterol-glucosyltransferase (SGT), flavonol synthase (FLS), and chalcone isomerase (CHI) involved in the tannin synthesis. These results would provide theoretical supports for the molecular markers developments and gene function studies related to the liquor-making traits, and the genetic improvement of waxy sorghum based on the genome editing technology.

genomics↗

bioRxiv2019

Metabolic remodeling by TIGAR overexpression is a therapeutic target in esophageal squamous-cell carcinoma

Whole-genome sequencing has identified many amplified genes in esophageal squamous-cell carcinoma (ESCC); however, their roles and the clinical relevance have yet elucidated. Here we show TP53-induced glycolysis and apoptosis regulator (TIGAR) is a major player in ESCC progression and chemoresistance. TIGAR reprograms glucose metabolism from glycolysis to the glutamine pathway through AMP-activated kinase, and its overexpression is correlated with poor disease outcomes. Tigar knockout mice have reduced ESCC growth and tumor burden. Treatment of TIGAR-overexpressed ESCC cell xenografts and patient-derived tumor xenografts in mice with combination of glutaminase inhibitor and chemotherapeutic agents achieves significant more efficacy than chemotherapy alone. These findings shed light on an important role of TIGAR in ESCC and provide evidence for targeted treatment of TIGAR-overexpressed ESCC.\n\nSignificanceEffective and target therapies are required for ESCC, one of the most common types of digestive OR cancer. Little has been known about the biology of ESCC progression or potential molecular targets OR for treatment. Whole-genome sequencing and RNA sequencing studies in ESCC have identified OR many recurrent copy number gain genes; however, the roles and druggable relevance of these OR genes remains poorly understood. Herein we demonstrate that TIGAR overexpression leads to OR metabolic remodeling, promoting ESCC progression and resistance to chemotherapeutic agents. OR Inhibiting the glutamine pathway significantly represses TIGAR-overexpressing ESCC growth and OR enhances tumor cell sensitization to cytotoxic agents. These findings might provide the rationale OR for clinical trials testing glutamine pathway inhibitors in combination with chemotherapy in OR TIGAR-expressing ESCC.

cancer biology↗

bioRxiv2019

Multilevel feedback architecture for adaptive regulation of learning in the insect brain

Modulatory (e.g. dopaminergic) neurons provide \"teaching signals\" that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of the Drosophila larva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one- and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

neuroscience↗

bioRxiv2019

Context-Aware Seeds for Read Mapping

MotivationMost modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers.\n\nResultsWe propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mapping but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 25.4% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver.\n\nAvailabilityhttps://github.com/Kingsford-Group/CAS_code

bioinformatics↗

bioRxiv2019

Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

Third-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100-4000 more (for 18 human datasets) or 1100-2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4-4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie). Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

bioinformatics↗

bioRxiv2017

Harnessing the Cross-talk between Tumor Cells and Tumor-associated Macrophages with a Nano-drug for modulation of Glioblastoma Immune Microenvironment

Glioblastoma (GBM) is the most frequent and malignant brain tumor with a high mortality rate. The presence of a large population of macrophages (M{varphi}) in the tumor microenvironment is a prominent feature of GBM and these so-called tumor-associated M{varphi} (TAM) closely interact with the GBM cells to promote the survival, progression and therapy resistance of the GBM. Various therapeutic strategies have been devised either targeting the GBM cells or the TAM but few have addressed the cross-talks between the two cell populations. The present study was carried out to explore the possibility of exploiting the cross-talks between the GBM cells (GC) and TAM for modulation of the GBM microenvironment through using Nano-DOX, a drug composite based on nanodiamonds bearing doxorubicin. In the in vitro work on human cell models, Nano-DOX-loaded TAM were first shown to be viable and able to infiltrate three-dimensional GC spheroids and release cargo drug therein. GC were then demonstrated to encourage Nano-DOX-loaded TAM to unload Nano-DOX back into GC which consequently emitted damage-associated molecular patterns (DAMPs) that are powerful immunostimulatory agents as well as indicators of cell damage. Nano-DOX was next proven to be a more potent inducer of GC DAMPs emission than doxorubicin. As a result, Nano-DOX-damaged GC exhibited an enhanced ability to attract both TAM and Nano-DOX-loaded TAM. Most remarkably, Nano-DOX-damaged GC reprogrammed the TAM from a pro-GBM phenotype to an anti-GBM phenotype that suppressed GC growth. Finally, the in vivo relevance of the in vitro findings was tested in animal study. Mice bearing orthotopic human GBM xenografts were intravenously injected with Nano-DOX-loaded mouse TAM which were found releasing drug in the GBM xenografts 24 h after injection. GC damage was evidenced by the induction of DAMPs emission within the xenografts and a shift of TAM phenotype was detected as well. Taken together, our results demonstrate a novel way with therapeutic potential to harness the cross-talk between GBM cells and TAM for modulation of the tumor immune microenvironment.\n\nAbbreviationsATP, adenosine triphosphate; BBB, blood-brain barrier; BCA, bicinchoninic acid; BMDM, bone marrow derived macrophages; CD, cluster of differentiation; CFSE, 5(6)-carboxyfluorescein diacetate, succinimidyl ester; CM, conditioned culture medium; CNS, central nervous system; CRT, calreticulin; DAMPs, damage-associated molecular patterns; DAB, diaminobenzidine; DOX, doxorubicin; ECL, enhanced chemiluminescence; ELISA, enzyme-linked immunosorbent assay; HMGB1, high mobility group protein B1; HSP90, heat shock protein 90; FACS, flow cytometry; GBM, glioblastoma; Guanylate Binding Protein 5 (GBP5); GC, glioblastoma cells; IHC, immunohistochemical; IL, interleukin; M{varphi}, macrophages; mBMDM, mouse BMDM; mBMDM2, Type-2 mBMDM; M1, Type-1 Mo; M2, Type-2 Mo; Nano-DOX, ND-PG-RGD-DOX; ND, nanodiamonds; Nano-DOX-mBMDM, Nano-DOX-loaded mouse BMDM; NGCM, Nano-DOX-treated-GC-conditioned medium; PBS, phosphate buffered saline; PG, polyglycerol; PMA, phorbol 12-myristate 13-acetate; PVDF, polyvinylidene fluoride; RGD, tripeptide of L-arginine, glycine and L-aspartic acid; RM, regular culture medium; SD, standard deviation; TAM, tumor-associated M{varphi}; TBST, Tris Buffered Saline with Tween(R) 20.\n\nGraphic abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC=\"FIGDIR/small/170282_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (111K):\norg.highwire.dtl.DTLVardef@180aea2org.highwire.dtl.DTLVardef@14922f7org.highwire.dtl.DTLVardef@96a696org.highwire.dtl.DTLVardef@92f050_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2017

SQUID: Transcriptomic Structural Variation Detection from RNA-seq

Transcripts are frequently modified by structural variations, which leads to a fused transcript of either multiple genes (known as a fusion gene) or a gene and a previously non-transcribing sequence. Detecting these modifications (called transcriptomic structural variations, or TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to accurately predict both fusion-gene and non-fusion-gene TSVs from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model, and doubles the accuracy on simulation data compared to other approaches. With SQUID, we identified novel non-fusion-gene TSVs on TCGA samples.

bioinformatics↗

bioRxiv2017

DeepBound: Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields

MotivationReconstructing the full-length expressed transcripts (a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.\n\nResultsWe present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods.\n\nAvailabilityDeepBound is freely available at https://github.com/realbigws/DeepBound.\n\nContactmingfu.shao@cs.cmu.edu, realbigws@gmail.com

bioinformatics↗

bioRxiv2017

Scallop Enables Accurate Assembly Of Transcripts Through Phasing-Preserving Graph Decomposition

We introduce Scallop, an accurate, reference-based transcript assembler for RNA-seq data. Scallop significantly improves reconstruction of multi-exon and lowly expressed transcripts. On 10 human samples aligned with STAR, Scallop produces (on average) 35.7% and 37.5% more correct multi-exon transcripts than two leading transcript assemblers, StringTie [1] and TransComb [2], respectively. For transcripts expressed at low levels in the same samples, Scallop assembles 65.2% and 50.2% more correct multi-exon transcripts than StringTie and TransComb, respectively. Scallop obtains this improvement through a novel algorithm that we prove preserves all phasing paths from reads (including paired-end reads), while also producing a parsimonious set of transcripts and minimizing coverage deviation.

bioinformatics↗

bioRxiv2017

Vegetally Localized Vrtn Functions As A Novel Repressor To Modulate bmp2b Transcription During Dorsoventral Patterning In Zebrafish

The vegetal pole cytoplasm represents a critical source of maternal signals for patterning the primary dorsoventral axis of the early embryo. Vegetally localized dorsal determinants are essential for the formation of the Spemann organizer, which expresses both bone morphogenetic proteins and their antagonists. The extracellular regulation of BMP signalling activity has been well characterized, however, transcriptional regulation of bmp genes along the dorsoventral axis remains largely unknown. Here, we report a novel mode of maternal regulation of BMP signalling in the lateral and ventral regions by analyzing the unexpected dorsalizing effect following vegetal yolk ablation experiment. We identified Vrtn as a novel vegetally localized maternal factor displaying dorsalizing activity. It functions as a novel transcriptional repressor to regulate bmp2b expression in the marginal region. Mechanistically, Vrtn binds bmp2b upstream sequence and inhibits its transcription independently of maternal Wnt/{beta}-catenin signalling. By creating vrtn loss-of-function mutation and analyzing maternal-zygotic mutant embryos, we further showed that Vrtn is required for the formation of dorsoventral axis. Our work thus unveils a novel maternal mechanism regulating BMP gradient during dorsoventral specification.

developmental biology↗

bioRxiv2016

Efficient Heuristic for Decomposing a Flow with Minimum Number of Paths

Motivated by transcript assembly and multiple genome assembly problems, in this paper, we study the following minimum path flow decomposition problem: given a directed acyclic graph G = (V,E) with source s and sink t and a flow f, compute a set of s-t paths P and assign weight w(p) for p [isin] P such that [Formula], and |P| is minimized. We propose an efficient pseudo-polynomialtime heuristic for this problem based on novel insights. Our heuristic gives a framework that consists of several components, providing a roadmap for continuing development of better heuristics. Through experimental studies on both simulated and transcript assembly instances, we show that our algorithm significantly improves the previous state-of-the-art algorithm. Implementation of our algorithm is available at https://github.com/Kingsford-Group/catfish.

bioinformatics↗