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Article ; Online: Automated splitting into batches for observational biomedical studies with sequential processing.

Burger, Bram / Vaudel, Marc / Barsnes, Harald

2022 Volume 24, Issue 4, Page(s) 1031–1044

Abstract: Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem ...

Abstract	Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.
MeSH term(s)	Humans ; Algorithms ; Research Design ; Observational Studies as Topic
Language	English
Publishing date	2022-04-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2031500-4
ISSN	1468-4357 ; 1465-4644
ISSN (online)	1468-4357
ISSN	1465-4644
DOI	10.1093/biostatistics/kxac014
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Article ; Online: Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides.

Skiadopoulou, Dafni / Vašíček, Jakub / Kuznetsova, Ksenia / Bouyssié, David / Käll, Lukas / Vaudel, Marc

Journal of proteome research

2023 Volume 22, Issue 10, Page(s) 3190–3199

Abstract: Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. ... ...

Abstract	Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.
Language	English
Publishing date	2023-09-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.3c00243
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Article ; Online: Smoking during pregnancy and its effect on placental weight: a Mendelian randomization study.

Jaitner, Annika / Vaudel, Marc / Tsaneva-Atanasova, Krasimira / Njølstad, Pål R / Jacobsson, Bo / Bowden, Jack / Johansson, Stefan / Freathy, Rachel M

BMC pregnancy and childbirth

2024 Volume 24, Issue 1, Page(s) 238

Abstract: Background: The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the ... ...

Abstract	Background: The causal relationship between maternal smoking in pregnancy and reduced offspring birth weight is well established and is likely due to impaired placental function. However, observational studies have given conflicting results on the association between smoking and placental weight. We aimed to estimate the causal effect of newly pregnant mothers quitting smoking on their placental weight at the time of delivery. Methods: We used one-sample Mendelian randomization, drawing data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (N = 690 to 804) and the Norwegian Mother, Father and Child Cohort Study (MoBa) (N = 4267 to 4606). The sample size depends on the smoking definition used for different analyses. The analysis was performed in pre-pregnancy smokers only, due to the specific role of the single-nucleotide polymorphism (SNP) rs1051730 (CHRNA5 - CHRNA3 - CHRNB4) in affecting smoking cessation but not initiation. Results: Fixed effect meta-analysis showed a 182 g [95%CI: 29,335] higher placental weight for pre-pregnancy smoking mothers who continued smoking at the beginning of pregnancy, compared with those who stopped smoking. Using the number of cigarettes smoked per day in the first trimester as the exposure, the causal effect on placental weight was 11 g [95%CI: 1,21] per cigarette per day. Similarly, smoking at the end of pregnancy was causally associated with higher placental weight. Using the residuals of birth weight regressed on placental weight as the outcome, we showed evidence of lower offspring birth weight relative to the placental weight, both for continuing smoking at the start of pregnancy as well as continuing smoking throughout pregnancy (change in z-score birth weight adjusted for z-score placental weight: -0.8 [95%CI: -1.6,-0.1]). Conclusion: Our results suggest that continued smoking during pregnancy causes higher placental weights.
MeSH term(s)	Child ; Female ; Pregnancy ; Humans ; Birth Weight/genetics ; Placenta ; Cohort Studies ; Longitudinal Studies ; Mendelian Randomization Analysis ; Smoking/adverse effects
Language	English
Publishing date	2024-04-04
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2059869-5
ISSN	1471-2393 ; 1471-2393
ISSN (online)	1471-2393
ISSN	1471-2393
DOI	10.1186/s12884-024-06431-0
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Article ; Online: Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes.

Kuznetsova, Ksenia G / Vašíček, Jakub / Skiadopoulou, Dafni / Molnes, Janne / Udler, Miriam / Johansson, Stefan / Njølstad, Pål Rasmus / Manning, Alisa / Vaudel, Marc

PloS one

2024 Volume 19, Issue 4, Page(s) e0300350

Abstract: Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the ... ...

Abstract	Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.
MeSH term(s)	Humans ; Proteomics ; Rare Diseases/genetics ; Genomics ; Computational Biology ; Diabetes Mellitus/genetics
Language	English
Publishing date	2024-04-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0300350
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Article ; Online: SearchGUI: A Highly Adaptable Common Interface for Proteomics Search and de Novo Engines.

Barsnes, Harald / Vaudel, Marc

Journal of proteome research

2018 Volume 17, Issue 7, Page(s) 2552–2555

Abstract: Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the ... ...

Abstract	Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.
MeSH term(s)	Algorithms ; Computational Biology ; Proteins/analysis ; Proteomics/methods ; Search Engine/methods ; Tandem Mass Spectrometry ; Workflow
Chemical Substances	Proteins
Language	English
Publishing date	2018-05-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.8b00175
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Article ; Online: Importance of Block Randomization When Designing Proteomics Experiments.

Burger, Bram / Vaudel, Marc / Barsnes, Harald

Journal of proteome research

2020 Volume 20, Issue 1, Page(s) 122–128

Abstract: Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block ... ...

Abstract	Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomization is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomization, and insights into how to effectively organize samples during experimental design, with special considerations with respect to proteomics.
MeSH term(s)	Proteomics ; Random Allocation ; Research Design
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.0c00536
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Article ; Online: PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data.

Farag, Yehia Mokhtar / Horro, Carlos / Vaudel, Marc / Barsnes, Harald

Journal of proteome research

2021 Volume 20, Issue 12, Page(s) 5419–5423

Abstract: Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the ... ...

Abstract	Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.
MeSH term(s)	Computational Biology/methods ; Internet ; Mass Spectrometry ; Proteomics/methods ; Software
Language	English
Publishing date	2021-10-28
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2078618-9
ISSN	1535-3907 ; 1535-3893
ISSN (online)	1535-3907
ISSN	1535-3893
DOI	10.1021/acs.jproteome.1c00678
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Article ; Online: Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis.

Schwämmle, Veit / Vaudel, Marc

Methods in molecular biology (Clifton, N.J.)

2017 Volume 1558, Page(s) 437–458

Abstract: Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient ... ...

Abstract	Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data.
Language	English
Publishing date	2017
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-6783-4_21
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Article ; Online: Finding haplotypic signatures in proteins.

Vašíček, Jakub / Skiadopoulou, Dafni / Kuznetsova, Ksenia G / Wen, Bo / Johansson, Stefan / Njølstad, Pål R / Bruckner, Stefan / Käll, Lukas / Vaudel, Marc

GigaScience

2023 Volume 12

Abstract: Background: The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode ... ...

Abstract	Background: The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown. Findings: Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches. Conclusions: As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.
MeSH term(s)	Proteomics/methods ; Haplotypes ; Reproducibility of Results ; Proteins/genetics ; Peptides
Chemical Substances	Proteins ; Peptides
Language	English
Publishing date	2023-11-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2708999-X
ISSN	2047-217X ; 2047-217X
ISSN (online)	2047-217X
ISSN	2047-217X
DOI	10.1093/gigascience/giad093
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Article ; Online: Extending protein interaction networks using proteoforms and small molecules.

Hernández Sánchez, Luis Francisco / Burger, Bram / Castro Campos, Rodrigo Alexander / Johansson, Stefan / Njølstad, Pål Rasmus / Barsnes, Harald / Vaudel, Marc

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 10

Abstract: Motivation: Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. ...

Abstract	Motivation: Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation. Results: We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability. Availability and implementation: Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.
Language	English
Publishing date	2023-09-25
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad598
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