scholarly journals Generating genomic platforms to study Candida albicans pathogenesis

2018 ◽  
Author(s):  
Mélanie Legrand ◽  
Sophie Bachellier-Bassi ◽  
Keunsook K. Lee ◽  
Yogesh Chaudhari ◽  
Hélène Tournu ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Large Scale ◽  
Expression Vectors ◽  
Proof Of Concept ◽  
Sequencing Data ◽  
Protein Protein Interaction ◽  
Functional Studies ◽  
Almost All ◽  
Genome Scale ◽  
Two Hybrid

ABSTRACTThe advent of the genomic era has made elucidating gene function at large scale a pressing challenge. ORFeome collections, whereby almost all ORFs of a given species are cloned and can be subsequently leveraged in multiple functional genomic approaches, represent valuable resources towards this endeavor. Here we provide novel, genome-scale tools for the study of Candida albicans, a commensal yeast that is also responsible for frequent superficial and disseminated infections in humans. We have generated an ORFeome collection composed of 5,102 ORFs cloned in a Gateway™ donor vector, representing 83% of the currently annotated coding sequences of C. albicans. Sequencing data of the cloned ORFs are available in the CandidaOrfDB database at http://candidaorfeome.eu. We also engineered 49 expression vectors with a choice of promoters, tags, and selection markers and demonstrated their applicability to the study of target ORFs transferred from the C. albicans ORFeome. In addition, the use of the ORFeome in the detection of protein-protein interaction was demonstrated. Mating-compatible strains as well as Gateway™-compatible two-hybrid vectors were engineered, validated and used in a proof of concept experiment. These unique and valuable resources should greatly facilitate future functional studies in C. albicans and the elucidation of mechanisms that underlie its pathogenicity.

On the structure of protein–protein interaction networks

2003 ◽  
Vol 31 (6) ◽  
pp. 1491-1496 ◽  
Author(s):  
A. Thomas ◽  
R. Cannings ◽  
N.A.M. Monk ◽  
C. Cannings
Keyword(s):  
Power Law ◽  
Protein Interactions ◽  
Large Scale ◽  
Underlying Structure ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approximate Power ◽  
Two Hybrid

We present a simple model for the underlying structure of protein–protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large-scale yeast two-hybrid screens of yeast protein–protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large-scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

scholarly journals Deconvolute individual genomes from metagenome sequences through short read clustering

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8966 ◽  
Author(s):  
Kexue Li ◽  
Yakang Lu ◽  
Li Deng ◽  
Lili Wang ◽  
Lizhen Shi ◽  
...  
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Short Reads ◽  
Clustering Problem ◽  
Metagenome Assembly ◽  
Real World Datasets ◽  
Almost All

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

scholarly journals CEN-tools: An integrative platform to identify the ‘contexts’ of essential genes

2020 ◽  
Author(s):  
Sumana Sharma ◽  
Cansu Dincer ◽  
Paula Weidemüller ◽  
Gavin J Wright ◽  
Evangelia Petsalaki
Keyword(s):  
Large Scale ◽  
Therapeutic Interventions ◽  
Genetic Screens ◽  
Protein Protein Interaction ◽  
Dependency Networks ◽  
A Cell ◽  
Genetic And Environmental Factors ◽  
Context Specific ◽  
Systematic Identification ◽  
Genome Scale

I.ABSTRACTAn emerging theme from large-scale genetic screens that identify genes essential for fitness of a cell, is that essentiality of a given gene is highly context-specific and depends on a number of genetic and environmental factors. Identification of such contexts could be the key to defining the function of the gene and also to develop novel therapeutic interventions. Here we present CEN-tools (Context-specific Essentiality Network-tools), a website and an accompanying python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels, and drug response levels. We show that CEN-tools is suitable for both the systematic identification of genetic dependencies as well as for targeted queries into the dependencies of specific user-selected genes. The associations between genes and a given context within CEN-tools are represented as dependency networks (CENs) and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

scholarly journals Deconvolute individual genomes from metagenome sequences through read clustering

2019 ◽  
Author(s):  
Kexue Li ◽  
Lili Wang ◽  
Lizhen Shi ◽  
Li Deng ◽  
Zhong Wang
Keyword(s):  
Large Scale ◽  
False Negative ◽  
Next Generation Sequencing Data ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Problem ◽  
Sequencing Coverage ◽  
Metagenome Assembly ◽  
Almost All ◽  
Small Clusters

ABSTRACTMotivationMetagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems.ResultsBased on a previously developed scalable read clustering method on Apache Spark, SpaRC, that has very low false positives, here we extended its capability by adding a new method to further cluster small clusters. This method exploits statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using a synthetic dataset from mouse gut microbiomes we show that this method has the potential to cluster almost all of the reads from genomes with sufficient sequencing coverage. We also explored several clustering parameters that deferentially affect genomes with various sequencing coverage.Availabilityhttps://bitbucket.org/berkeleylab/jgi-sparc/[email protected]

Negative protein–protein interaction datasets derived from large-scale two-hybrid experiments

Methods ◽  
2012 ◽  
Vol 58 (4) ◽  
pp. 343-348 ◽  
Author(s):  
Leonardo G. Trabuco ◽  
Matthew J. Betts ◽  
Robert B. Russell
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Protein Protein Interaction ◽  
Two Hybrid

scholarly journals OFP1 Interaction with ATH1 Regulates Stem Growth, Flowering Time and Flower Basal Boundary Formation in Arabidopsis

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 399 ◽  
Author(s):  
Liguo Zhang ◽  
Lili Sun ◽  
Xiaofei Zhang ◽  
Shuquan Zhang ◽  
Dongwei Xie ◽  
...  
Keyword(s):  
Flowering Time ◽  
Large Scale ◽  
Stem Elongation ◽  
Stem Growth ◽  
Bimolecular Fluorescence Complementation ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
And Control ◽  
Two Hybrid

Ovate Family Protein1 (OFP1) is a regulator, and it is suspected to be involved in plant growth and development. Meanwhile, Arabidopsis Thaliana Homeobox (ATH1), a BEL1-like homeodomain (HD) transcription factor, is known to be involved in regulating stem growth, flowering time and flower basal boundary development in Arabidopsis. Previous large-scale yeast two-hybrid studies suggest that ATH1 possibly interact with OFP1, but this interaction is yet unverified. In our study, the interaction of OFP1 with ATH1 was verified using a directional yeast two-hybrid system and bimolecular fluorescence complementation (BiFC). Our results also demonstrated that the OFP1-ATH1 interaction is mainly controlled by the HD domain of ATH1. Meanwhile, we found that ATH1 plays the role of transcriptional repressor to regulate plant development and that OFP1 can enhance ATH1 repression function. Regardless of the mechanism, a putative functional role of ATH1-OFP1 may be to regulate the expression of the both the GA20ox1 gene, which is involved in gibberellin (GA) biosynthesis and control of stem elongation, and the Flowering Locus C (FLC) gene, which inhibits transition to flowering. Ultimately, the regulatory functional mechanism of OFP1-ATH1 may be complicated and diverse according to our results, and this work lays groundwork for further understanding of a unique and important protein–protein interaction that influences flowering time, stem development, and flower basal boundary development in plants.

scholarly journals Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Enrique Blanco ◽  
Mar González-Ramírez ◽  
Luciano Di Croce
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Graphical Analysis ◽  
Sequencing Data ◽  
Efficient Manner ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Almost All ◽  
User Friendly

AbstractLarge-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

scholarly journals High resolution protein-protein interaction mapping using all-versus-all sequencing (AVA-seq)

2018 ◽  
Author(s):  
Simeon S. Andrews ◽  
Stephanie Schaefer-Ramadan ◽  
Nayra M. Al-Thani ◽  
Ikhlak Ahmed ◽  
Yasmin A. Mohamoud ◽  
...  
Keyword(s):  
High Resolution ◽  
Hybrid Systems ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Specific Protein ◽  
Next Generation ◽  
Protein Protein Interaction ◽  
Contact Points ◽  
Two Hybrid

AbstractTwo-hybrid systems test for protein-protein interactions and can provide important information for genes with unknown function. Despite their success, two-hybrid systems have remained mostly untouched by improvements from next-generation DNA sequencing. Here we present a method for all-versus-all protein interaction mapping (AVA-seq) that utilizes next-generation sequencing to remove multiple bottlenecks of the two-hybrid process. The method allows for high resolution protein-protein interaction mapping of a small set of proteins, or the potential for lower-resolution mapping of entire proteomes. Features of the system include open-reading frame selection to improve efficiency, high bacterial transformation efficiency, a convergent fusion vector to allow paired-end sequencing of interactors, and the use of protein fragments rather than full-length genes to better resolve specific protein contact points. We demonstrate the system’s strengths and limitations on a set of proteins known to interact in humans and provide a framework for future large-scale projects.

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