Journal: Nature cell biology

Article Title: RNA-targeting CRISPR systems from metagenomic discovery to transcriptomic engineering

doi: 10.1038/s41556-019-0454-7

Figure Lengend Snippet: a, Cas proteins, including RCas, are discovered in metagenomic sequencing of bacteria and archaea using an established bioinformatic pipeline. Starting with a CRISPR array search seed, nearby putative Cas effectors are filtered on the basis of various criteria, including co-correlation with CRISPR arrays, size, and predicted protein domains. After grouping based on homology, distinct groups of effectors are selected for experimental validation follow-up. Researchers deduce the targeting substrate and mechanism through biochemical incubation of substrate nucleic acids with purified CRISPR ribonucleoprotein complexes and by observing resultant cleavage and binding. Genetic interrogation of targeting rules can be achieved through a spacer depletion screen assay consisting of a CRISPR library with spacers targeting an antibiotic resistance gene. Bacterial co-transformation via plasmids containing (i) the spacer library and Cas effector and (ii) the antibiotic resistance gene, followed by computational analysis of depleted spacers in the sequenced surviving population, will yield any targeting-dependent sequence requirements vis-à-vis the spacer. b, Type II and VI systems, encapsulating Cas9 and Cas13, respectively, represent the most commonly used RCas systems. Cas9 binds to a tracrRNA and crRNA (often combined into a single sgRNA) to target RNA. Depending on the Cas9 variant, a PAMmer oligonucleotide comprising a PAM sequence may be required for RNA binding10 and an additional domain (such as PIN domain endonuclease) may be required for RNA cleavage47. Cas13 binds to a crRNA for effective RNA targeting and subsequent indiscriminate cleavage. c, Prokaryotic Argonaute (pAgo) systems resemble CRISPR-Cas systems in their ability to target nucleic acids with a programmable guide. They exist as part of a broader RNA-targeting ecosystem that includes antisense oligonucleotides (ASOs), RNA interference (RNAi), CRISPR-Cas-inspired RNA targeting systems (CIRTS), viral coat proteins (VCPs), zinc fingers, Pumilio and FBF homology proteins (PUFs), and pentatricopeptide repeat proteins (PPRs), among others.

Article Snippet: Competing interests A.A.S. declares inventorship on the following published patents, applied for by the Broad Institute of MIT and Harvard and the Massachusetts Institute of Technology: WO2018035250A1 on methods for bioinformatic discovery of class 2 CRISPR-Cas systems; WO2017070605 on systems, methods, and compositions for targeting nucleic acids with type VI-B CRISPR-Cas systems.

Techniques: Sequencing, Bacteria, CRISPR, Biomarker Discovery, Incubation, Purification, Binding Assay, Transformation Assay, Variant Assay, Zinc-Fingers

Journal: Bioinformatics

Article Title: Training for translation between disciplines: a philosophy for life and data sciences curricula

doi: 10.1093/bioinformatics/bty233

Figure Lengend Snippet: Conceptual organization of bioinformatics and systems biology education along three key elements: balance, translate and focus

Article Snippet: This necessity is mirrored at the educational level, but training programmes in computational biology, bioinformatics and systems biology (BioSB) seem to be lagging behind this development ( Alves et al. , 2011 ; Attwood et al. , 2015 ), despite continued efforts, such as the PLoS Computational Biology Education collection which has already been available for more than a decade ( , ).

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Journal: Bioinformatics

Article Title: Training for translation between disciplines: a philosophy for life and data sciences curricula

doi: 10.1093/bioinformatics/bty233

Figure Lengend Snippet: Implementation of balance-translate-focus throughout the bioinformatics and systems biology curriculum

Article Snippet: This necessity is mirrored at the educational level, but training programmes in computational biology, bioinformatics and systems biology (BioSB) seem to be lagging behind this development ( Alves et al. , 2011 ; Attwood et al. , 2015 ), despite continued efforts, such as the PLoS Computational Biology Education collection which has already been available for more than a decade ( , ).

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Journal: Bioinformatics

Article Title: Training for translation between disciplines: a philosophy for life and data sciences curricula

doi: 10.1093/bioinformatics/bty233

Figure Lengend Snippet: Diversity of incoming students by topic area, over six cohorts from 2011 to 2016; the vertical axis shows fraction of students. Counting individual programme topics, bioinformatics bachelors are the largest fraction (here counted with together ‘biotech’), but on average bioinformaticians account for only 12% of all students. In recent years, biomedical bachelors are starting to dominate the influx, recently accounting for about one-third of new students

Article Snippet: This necessity is mirrored at the educational level, but training programmes in computational biology, bioinformatics and systems biology (BioSB) seem to be lagging behind this development ( Alves et al. , 2011 ; Attwood et al. , 2015 ), despite continued efforts, such as the PLoS Computational Biology Education collection which has already been available for more than a decade ( , ).

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Journal: Bioinformatics

Article Title: Training for translation between disciplines: a philosophy for life and data sciences curricula

doi: 10.1093/bioinformatics/bty233

Figure Lengend Snippet: Correlation between grades in the maths and programming classes, and subsequent course exam grades, based on 2015–2016 grades. A —Grades of the maths class correlate well with exam grades of fundamentals of bioinformatics (FoB) and introduction to systems biology (ISB). B —Correlation of maths grades with bioinformatics courses, FoB and the later structural bioinformatics course, is also high. C —For the programming class grades, this correlation is much lower, here shown for FoB and ISB

Article Snippet: This necessity is mirrored at the educational level, but training programmes in computational biology, bioinformatics and systems biology (BioSB) seem to be lagging behind this development ( Alves et al. , 2011 ; Attwood et al. , 2015 ), despite continued efforts, such as the PLoS Computational Biology Education collection which has already been available for more than a decade ( , ).

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Journal: Bioinformatics

Article Title: Training for translation between disciplines: a philosophy for life and data sciences curricula

doi: 10.1093/bioinformatics/bty233

Figure Lengend Snippet: The intended learning outcomes of the programme are structured along the three principle elements: balance, translate and focus

Article Snippet: This necessity is mirrored at the educational level, but training programmes in computational biology, bioinformatics and systems biology (BioSB) seem to be lagging behind this development ( Alves et al. , 2011 ; Attwood et al. , 2015 ), despite continued efforts, such as the PLoS Computational Biology Education collection which has already been available for more than a decade ( , ).

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Journal: Investigative Ophthalmology & Visual Science

Article Title: Oxidative Stress and the Regulation of Complement Activation in Human Glaucoma

doi: 10.1167/iovs.10-5289

Figure Lengend Snippet: Integration of the identified proteins into canonical complement activation pathways using bioinformatics analysis tools (Ingenuity Pathways Knowledge Base; Ingenuity Systems). Blue: proteins identified by the LC-MS/MS analysis of human retinal proteins. Yellow: additional proteins detected in the human retina by immunohistochemical analysis using specific antibodies.

Article Snippet: Proteomic Analysis of Complement Activation in Human Glaucoma fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1. caption a7 Integration of the identified proteins into canonical complement activation pathways using bioinformatics analysis tools (Ingenuity Pathways Knowledge Base; Ingenuity Systems).

Techniques: Activation Assay, Liquid Chromatography with Mass Spectroscopy, Immunohistochemical staining

Journal: Investigative Ophthalmology & Visual Science

Article Title: Oxidative Stress and the Regulation of Complement Activation in Human Glaucoma

doi: 10.1167/iovs.10-5289

Figure Lengend Snippet: Bioinformatics analysis of complement regulation. Bioinformatics analysis of the mass spectrometric data (using the Ingenuity Pathways Analysis System) established extended networks of signaling molecules associated with complement regulation in glaucoma. In this extended high-probability network, blue shows the proteins out of thousands of proteins identified in the human retinal proteome. Detailed information for abbreviated proteins is available at http://www.ncbi.nlm.nih.gov/protein.

Article Snippet: Proteomic Analysis of Complement Activation in Human Glaucoma fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1. caption a7 Integration of the identified proteins into canonical complement activation pathways using bioinformatics analysis tools (Ingenuity Pathways Knowledge Base; Ingenuity Systems).

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