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Multiple Major Scientific Journals Will Fully Adopt Open Access Under Plan S

Curator: Stephen J. Williams, PhD

More university library systems have been pressuring major scientific publishing houses to adopt an open access strategy in order to reduce the library system’s budgetary burdens.  In fact some major universities like the California system of universities (University of California and other publicly funded universities in the state as well as Oxford University in the UK, even MIT have decided to become their own publishing houses in a concerted effort to fight back against soaring journal subscription costs as well as the costs burdening individual scientists and laboratories (some of the charges to publish one paper can run as high as $8000.00 USD while the journal still retains all the rights of distribution of the information).  Therefore more and more universities, as well as concerted efforts by the European Union and the US government are mandating that scientific literature be published in an open access format.

The results of this pressure are evident now as major journals like Nature, JBC, and others have plans to go fully open access in 2021.  Below is a listing and news reports of some of these journals plans to undertake a full Open Access Format.

 

Nature to join open-access Plan S, publisher says

09 APRIL 2020 UPDATE 14 APRIL 2020

Springer Nature says it commits to offering researchers a route to publishing open access in Nature and most Nature-branded journals from 2021.

Richard Van Noorden

After a change in the rules of the bold open-access (OA) initiative known as Plan S, publisher Springer Nature said on 8 April that many of its non-OA journals — including Nature — were now committed to joining the plan, pending discussion of further technical details.

This means that Nature and other Nature-branded journals that publish original research will now look to offer an immediate OA route after January 2021 to scientists who want it, or whose funders require it, a spokesperson says. (Nature is editorially independent of its publisher, Springer Nature.)

“We are delighted that Springer Nature is committed to transitioning its journals to full OA,” said Robert Kiley, head of open research at the London-based biomedical funder Wellcome, and the interim coordinator for Coalition S, a group of research funders that launched Plan S in 2018.

But Lisa Hinchliffe, a librarian at the University of Illinois at Urbana–Champaign, says the changed rules show that publishers have successfully pushed back against Plan S, softening its guidelines and expectations — in particular in the case of hybrid journals, which publish some content openly and keep other papers behind paywalls. “The coalition continues to take actions that rehabilitate hybrid journals into compliance rather than taking the hard line of unacceptability originally promulgated,” she says.

 

 

 

 

What is Plan S?

The goal of Plan S is to make scientific and scholarly works free to read as soon as they are published. So far, 17 national funders, mostly in Europe, have joined the initiative, as have the World Health Organization and two of the world’s largest private biomedical funders — the Bill & Melinda Gates Foundation and Wellcome. The European Commission will also implement an OA policy that is aligned with Plan S. Together, this covers around 7% of scientific articles worldwide, according to one estimate. A 2019 report published by the publishing-services firm Clarivate Analytics suggested that 35% of the research content published in Nature in 2017 acknowledged a Plan S funder (see ‘Plan S papers’).

PLAN S PAPERS

Journal Total papers in 2017 % acknowledging Plan S funder
Nature 290 35%
Science 235 31%
Proc. Natl Acad. Sci. USA 639 20%

Source: The Plan S footprint: Implications for the scholarly publishing landscape (Institute for Scientific Information, 2019)

 

Source: https://www.nature.com/articles/d41586-020-01066-5

Opening ASBMB publications freely to all

 

Lila M. Gierasch, Editor-in-Chief, Journal of Biological Chemistry

Nicholas O. Davidson

Kerry-Anne Rye, Editors-in-Chief, Journal of Lipid Research and 

Alma L. Burlingame, Editor-in-Chief, Molecular and Cellular Proteomics

 

We are extremely excited to announce on behalf of the American Society for Biochemistry and Molecular Biology (ASBMB) that the Journal of Biological Chemistry (JBC), Molecular & Cellular Proteomics (MCP), and the Journal of Lipid Research (JLR) will be published as fully open-access journals beginning in January 2021. This is a landmark decision that will have huge impact for readers and authors. As many of you know, many researchers have called for journals to become open access to facilitate scientific progress, and many funding agencies across the globe are either already requiring or considering a requirement that all scientific publications based on research they support be published in open-access journals. The ASBMB journals have long supported open access, making the accepted author versions of manuscripts immediately and permanently available, allowing authors to opt in to the immediate open publication of the final version of their paper, and endorsing the goals of the larger open-access movement (1). However, we are no longer satisfied with these measures. To live up to our goals as a scientific society, we want to freely distribute the scientific advances published in JBC, MCP, and JLR as widely and quickly as possible to support the scientific community. How better can we facilitate the dissemination of new information than to make our scientific content freely open to all?

For ASBMB journals and others who have contemplated or made the transition to publishing all content open access, achieving this milestone generally requires new financial mechanisms. In the case of the ASBMB journals, the transition to open access is being made possible by a new partnership with Elsevier, whose established capabilities and economies of scale make the costs associated with open-access publication manageable for the ASBMB (2). However, we want to be clear: The ethos of ASBMB journals will not change as a consequence of this new alliance. The journals remain society journals: The journals are owned by the society, and all scientific oversight for the journals will remain with ASBMB and its chosen editors. Peer review will continue to be done by scientists reviewing the work of scientists, carried out by editorial board members and external referees on behalf of the ASBMB journal leadership. There will be no intervention in this process by the publisher.

Although we will be saying “goodbye” to many years of self-publishing (115 in the case of JBC), we are certain that we are taking this big step for all the right reasons. The goal for JBC, MCP, and JLR has always been and will remain to help scientists advance their work by rapidly and effectively disseminating their results to their colleagues and facilitating the discovery of new findings (13), and open access is only one of many innovations and improvements in science publishing that could help the ASBMB journals achieve this goal. We have been held back from fully exploring these options because of the challenges of “keeping the trains running” with self-publication. In addition to allowing ASBMB to offer all the content in its journals to all readers freely and without barriers, the new partnership with Elsevier opens many doors for ASBMB publications, from new technology for manuscript handling and production, to facilitating reader discovery of content, to deploying powerful analytics to link content within and across publications, to new opportunities to improve our peer review mechanisms. We have all dreamed of implementing these innovations and enhancements (45) but have not had the resources or infrastructure needed.

A critical aspect of moving to open access is how this decision impacts the cost to authors. Like most publishers that have made this transition, we have been extremely worried that achieving open-access publishing would place too big a financial burden on our authors. We are pleased to report the article-processing charges (APCs) to publish in ASBMB journals will be on the low end within the range of open-access fees: $2,000 for members and $2,500 for nonmembers. While slightly higher than the cost an author incurs now if the open-access option is not chosen, these APCs are lower than the current charges for open access on our existing platform.

References

1.↵ Gierasch, L. M., Davidson, N. O., Rye, K.-A., and Burlingame, A. L. (2019) For the sake of science. J. Biol. Chem. 294, 2976 FREE Full Text

2.↵ Gierasch, L. M. (2017) On the costs of scientific publishing. J. Biol. Chem. 292, 16395–16396 FREE Full Text

3.↵ Gierasch, L. M. (2020) Faster publication advances your science: The three R’s. J. Biol. Chem. 295, 672 FREE Full Text

4.↵ Gierasch, L. M. (2017) JBC is on a mission to facilitate scientific discovery. J. Biol. Chem. 292, 6853–6854 FREE Full Text

5.↵ Gierasch, L. M. (2017) JBC’s New Year’s resolutions: Check them off! J. Biol. Chem. 292, 21705–21706 FREE Full Text

 

Source: https://www.jbc.org/content/295/22/7814.short?ssource=mfr&rss=1

 

Open access publishing under Plan S to start in 2021

BMJ

2019; 365 doi: https://doi.org/10.1136/bmj.l2382 (Published 31 May 2019)Cite this as: BMJ 2019;365:l2382

From 2021, all research funded by public or private grants should be published in open access journals, according to a group of funding agencies called coALition S.1

The plan is the final version of a draft that was put to public consultation last year and attracted 344 responses from institutions, almost half of them from the UK.2 The responses have been considered and some changes made to the new system called Plan S, a briefing at the Science Media Centre in London was told on 29 May.

The main change has been to delay implementation for a year, to 1 January 2021, to allow more time for those involved—researchers, funders, institutions, publishers, and repositories—to make the necessary changes, said John-Arne Røttingen, chief executive of the Research Council of Norway.

“All research contracts signed after that date should include the obligation to publish in an open access journal,” he said. T……

(Please Note in a huge bit of irony this article is NOT Open Access and behind a paywall…. Yes an article about an announcement to go Open Access is not Open Access)

Source: https://www.bmj.com/content/365/bmj.l2382.full

 

 

Plan S

From Wikipedia, the free encyclopedia

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Not to be confused with S-Plan.

Plan S is an initiative for open-access science publishing launched in 2018[1][2] by “cOAlition S”,[3] a consortium of national research agencies and funders from twelve European countries. The plan requires scientists and researchers who benefit from state-funded research organisations and institutions to publish their work in open repositories or in journals that are available to all by 2021.[4] The “S” stands for “shock”.[5]

Principles of the plan[edit]

The plan is structured around ten principles.[3] The key principle states that by 2021, research funded by public or private grants must be published in open-access journals or platforms, or made immediately available in open access repositories without an embargo. The ten principles are:

  1. authors should retain copyrighton their publications, which must be published under an open license such as Creative Commons;
  2. the members of the coalition should establish robust criteria and requirements for compliant open access journals and platforms;
  3. they should also provide incentives for the creation of compliant open access journals and platforms if they do not yet exist;
  4. publication fees should be covered by the funders or universities, not individual researchers;
  5. such publication fees should be standardized and capped;
  6. universities, research organizations, and libraries should align their policies and strategies;
  7. for books and monographs, the timeline may be extended beyond 2021;
  8. open archives and repositories are acknowledged for their importance;
  9. hybrid open-access journalsare not compliant with the key principle;
  10. members of the coalition should monitor and sanction non-compliance.

Member organisations

Organisations in the coalition behind Plan S include:[14]

International organizations that are members:

Plan S is also supported by:

 

Other articles on Open Access on this Open Access Journal Include:

MIT, guided by open access principles, ends Elsevier negotiations, an act followed by other University Systems in the US and in Europe

 

Open Access e-Scientific Publishing: Elected among 2018 Nature’s 10 Top Influencers – ROBERT-JAN SMITS: A bureaucrat launched a drive to transform science publishing

 

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

 

Mozilla Science Lab Promotes Data Reproduction Through Open Access: Report from 9/10/2015 Online Meeting

 

Elsevier’s Mendeley and Academia.edu – How We Distribute Scientific Research: A Case in Advocacy for Open Access Journals

 

The Fatal Self Distraction of the Academic Publishing Industry: The Solution of the Open Access Online Scientific Journals
PeerJ Model for Open Access Scientific Journal
“Open Access Publishing” is becoming the mainstream model: “Academic Publishing” has changed Irrevocably
Open-Access Publishing in Genomics

 

 

 

 

 

 

 

 

 

 

 

 

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Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 11/15/2018

The following post will be an ongoing curation of reviews of gene variant bioinformatic software.

 

The Ensembl Variant Effect Predictor.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F.

Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.

Author information

1

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.

2

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

3

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

 

Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015).  However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here).  To this effect major sequencing projects have been initiated worldwide including:

A more thorough curation of sequencing projects can be seen in the following post:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein.  Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.

To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.

Summary of Features

  • Annotation: VEP can annotate two broad categories of genomic variants
    • Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
    • Larger structural variants > 50 nucleotides
  • Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
  • Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
  • Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
  • Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation.  Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
  • Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants.  Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation.  Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
  • Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats.  Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
  • Choice of user interface
    • Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
    • VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub.  The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
    • VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.

 

 

Watch Video on VES Instructional Webinar: https://youtu.be/7Fs7MHfXjWk

Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP

 

 

Availability of data and materials

The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.

 

References

Large-scale discovery of novel genetic causes of developmental disorders.

Deciphering Developmental Disorders Study.

Nature2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962

Updated 11/15/2018

 

Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools

Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:

  • For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
  • As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
  • For rare diseases, pathogenic allele frequencies are generally low

Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.

In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene.  These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007).  It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).

 

References

Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.

Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.

Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.

Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.

 

Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Andersen LL1Terczyńska-Dyla E1Mørk N2Scavenius C1Enghild JJ1Höning K3Hornung V3,4Christiansen M5,6Mogensen TH2,6Hartmann R7.

 

Abstract

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Methodology

  1. Identification of rare variants
  2. Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
  3. Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
  4. In-silico predictions: Variants classified by the in silico variant pathogenicity prediction programs: SIFT, Mutation Assessor, FATHMM, PROVEAN, SNAP2, PolyPhen2, PhD-SNP, SNP&GO, FATHMM-MKL, MutationTaster2, PredictSNP, Condel, MetaSNP, and CADD. Each program returned prediction scores measuring likelihood of a variant either being ‘deleterious’ or ‘neutral’. Prediction accuracy measured as

ACC = (true positive+true negative)/(true positive+true negative+false positive+false negative)

 

  1. Validation of prediction software/tools

In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function.  Western blot was performed to confirm the expression of the constructs.

 

Results

Table 2 Summary of the
bioinformatic predictions
HSE variants IFNLR1 variants Overall ACC
TN TP FN FP Total ACC TN TP FN FP Total ACC
Uniform cutoff
SIFT 4 1 0 4 9 0.56 8 1 0 7 16 0.56 0.56
Mutation assessor 6 1 0 2 9 0.78 9 1 0 6 16 0.63 0.68
FATHMM 7 1 0 1 9 0.89 0.89
PROVEAN 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
SNAP2 5 1 0 3 9 0.67 8 0 1 7 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 12 1 0 3 16 0.81 0.80
PhD-SNP 7 1 0 1 9 0.89 11 1 0 4 16 0.75 0.80
SNPs&GO 8 1 0 0 9 1.00 14 1 0 1 16 0.94 0.96
FATHMM MKL 4 1 0 4 9 0.56 13 0 1 2 16 0.81 0.72
MutationTaster2 4 0 1 4 9 0.44 14 0 1 1 16 0.88 0.72
PredictSNP 6 1 0 2 9 0.78 11 1 0 4 16 0.75 0.76
Condel 6 1 0 2 9 0.78 0.78
Meta-SNP 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
CADD 2 1 0 6 9 0.33 8 0 1 7 16 0.50 0.44
MSC 95% cutoff
SIFT 5 1 0 3 9 0.67 8 1 0 8 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 13 1 0 3 16 0.81 0.80
CADD 4 1 0 4 9 0.56 7 0 1 9 16 0.44 0.48

 

Note: TN: true negative, TP: true positive, FN: false negative, FP: false positive, ACC: accuracy

Functional testing (data obtained from reporter construct experiments) were considered as the correct outcome.

Three prediction tools (PROVEAN, SNP&GO, and MetaSNP correctly predicted the effect of all nine variants tested.

 

Other articles related to Genomics and Bioinformatics on this online Open Access Journal Include:

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

 

Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes

 

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

 

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

 

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