Endre søk
Begrens søket
1 - 9 of 9
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1. Almagro Armenteros, Jose Juan
    et al.
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Emanuelsson, Olof
    Winther, Ole
    von Heijne, Gunnar
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Nielsen, Henrik
    Detecting Novel Sequence Signals in Targeting Peptides Using Deep LearningManuskript (preprint) (Annet vitenskapelig)
  • 2. Armenteros, Jose Juan Almagro
    et al.
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Emanuelsson, Olof
    Winther, Ole
    von Heijne, Gunnar
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Nielsen, Henrik
    Detecting sequence signals in targeting peptides using deep learning2019Inngår i: Life science alliance, E-ISSN 2575-1077, Vol. 2, nr 5, artikkel-id UNSP e201900429Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.

  • 3.
    Basile, Walter
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Bassot, Claudio
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
    Why do eukaryotic proteins contain more intrinsically disordered regions?2019Inngår i: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, nr 7, artikkel-id e1007186Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Intrinsic disorder is more abundant in eukaryotic than prokaryotic proteins. Methods predicting intrinsic disorder are based on the amino acid sequence of a protein. Therefore, there must exist an underlying difference in the sequences between eukaryotic and prokaryotic proteins causing the (predicted) difference in intrinsic disorder. By comparing proteins, from complete eukaryotic and prokaryotic proteomes, we show that the difference in intrinsic disorder emerges from the linker regions connecting Pfam domains. Eukaryotic proteins have more extended linker regions, and in addition, the eukaryotic linkers are significantly more disordered, 38% vs. 12-16% disordered residues. Next, we examined the underlying reason for the increase in disorder in eukaryotic linkers, and we found that the changes in abundance of only three amino acids cause the increase. Eukaryotic proteins contain 8.6% serine; while prokaryotic proteins have 6.5%, eukaryotic proteins also contain 5.4% proline and 5.3% isoleucine compared with 4.0% proline and ≈ 7.5% isoleucine in the prokaryotes. All these three differences contribute to the increased disorder in eukaryotic proteins. It is tempting to speculate that the increase in serine frequencies in eukaryotes is related to regulation by kinases, but direct evidence for this is lacking. The differences are observed in all phyla, protein families, structural regions and type of protein but are most pronounced in disordered and linker regions. The observation that differences in the abundance of three amino acids cause the difference in disorder between eukaryotic and prokaryotic proteins raises the question: Are amino acid frequencies different in eukaryotic linkers because the linkers are more disordered or do the differences cause the increased disorder?

  • 4.
    Basile, Walter
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center (SeRC), Sweden.
    The classification of orphans is improved by combining searches in both proteomes and genomes2017Manuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The identification of de novo created genes is important as it provides a glimpse on the evolutionary processes of gene creation. Potential de novo created genes are identified by selecting genes that have no homologs outside a particular species, but for an accurate detection this identification needs to be correct.

    Genes without any homologs are often referred to as orphans; in addition to de novo created ones, fast evolving genes or genes lost in all related genomes might also be classified as orphans. The identification of orphans is dependent on: (i) a method to detect homologs and (ii) a database including genes from related genomes.

    Here, we set out to investigate how the detection of orphans is influenced by these two factors. Using Saccharomyces cerevisiae we identify that best strategy is to use a combination of searching annotated proteins and a six-frame translation of all ORFs from closely related genomes. Using this strategy we obtain a set of 54 orphans in Drosophila melanogaster and 38 in Drosophila pseudoobscura, significantly less than what is reported in some earlier studies.

  • 5. Hatos, Andras
    et al.
    Hajdu-Soltesz, Borbala
    Monzon, Alexander M.
    Palopoli, Nicolas
    Alvarez, Lucia
    Aykac-Fas, Burcu
    Bassot, Claudio
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Benitez, Guillermo
    Bevilacqua, Martina
    Chasapi, Anastasia
    Chemes, Lucia
    Davey, Norman E.
    Davidovic, Radoslav
    Dunker, A. Keith
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Gobeill, Julien
    Gonzalez Foutel, Nicolas S.
    Sudha, Govindarajan
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Guharoy, Mainak
    Horvath, Tamas
    Iglesias, Valentin
    Kajava, Andrey
    Kovacs, Orsolya P.
    Lamb, John
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Lambrughi, Matteo
    Lazar, Tamas
    Leclercq, Jeremy Y.
    Leonardi, Emanuela
    Macedo-Ribeiro, Sandra
    Macossay-Castillo, Mauricio
    Maiani, Emiliano
    Manso, Jose A.
    Marino-Buslje, Cristina
    Martinez-Perez, Elizabeth
    Meszaros, Balint
    Micetic, Ivan
    Minervini, Giovanni
    Murvai, Nikoletta
    Necci, Marco
    Ouzounis, Christos A.
    Pajkos, Matyas
    Paladin, Lisanna
    Pancsa, Rita
    Papaleo, Elena
    Parisi, Gustavo
    Pasche, Emilie
    Barbosa Pereira, Pedro J.
    Promponas, Vasilis J.
    Pujols, Jordi
    Quaglia, Federica
    Ruch, Patrick
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Schad, Eva
    Szabo, Beata
    Szaniszlo, Tamas
    Tamana, Stella
    Tantos, Agnes
    Veljkovic, Nevena
    Ventura, Salvador
    Vranken, Wim
    Dosztanyi, Zsuzsanna
    Tompa, Peter
    Tosatto, Silvio C. E.
    Piovesan, Damiano
    DisProt: intrinsic protein disorder annotation in 20202020Inngår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 48, nr D1, s. D269-D276Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Database of Protein Disorder (DisProt, URL:https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome.

  • 6.
    Salvatore, Marco
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Science for Life Laboratory.
    Predicting the route: from protein sequence to sorting in eukaryotic cell2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Proteins need to be localised in the correct compartment of a eukaryotic cell to function correctly. Therefore, a protein needs to be transported to the right location. Specific signals present in the protein sequence direct proteins to different subcellular localisations. The correct transport is essential for the life of the cell, while, possible errors during the transport can cause irreversible damage and interfere with the activities of surrounding proteins. For more than 30 years, the development of methods to identify the localisation of proteins using both experimental and computational approaches has been an important research area. The objective of this thesis is to develop better computational methods for the classification of the subcellular localisation of eukaryotic proteins. I first describe the development of a consensus method, SubCons, which improves the subcellular prediction of human proteins. Next, I present the SubCons web-server as well as an additional benchmark using protein annotation from novel mass-spectrometry studies in two eukaryotic organisms Mus musculus and Drosophila melanogaster. Then, I present the new version of TargetP and how deep learning can improve the identification of N-terminal sorting signals by focusing on relevant biological signatures. Finally, I describe the development of a novel method for sub-nuclear localisation prediction. Here, I show that the performance of a deep convolutional neural network is improved when using an augmented dataset of homologous proteins.

    Fulltekst (pdf)
    Predicting the route: from protein sequence to sorting in eukaryotic cell
    Download (jpg)
    Omslagsframsida
  • 7.
    Salvatore, Marco
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Almagro Armenteros, Jose Juan
    Lamb, John
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Mileti, Enrichetta
    Winther, Ole
    Nielsen, Henrik
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.
    Improved sub-nuclear prediction by Deep Learning using an augmented datasetManuskript (preprint) (Annet vitenskapelig)
  • 8.
    Salvatore, Marco
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction2018Inngår i: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 27, nr 1, s. 195-201Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    SubCons is a recently developed method that predicts the subcellular localization of a protein. It combines predictions from four predictors using a Random Forest classifier. Here, we present the user-friendly web-interface implementation of SubCons. Starting from a protein sequence, the server rapidly predicts the subcellular localizations of an individual protein. In addition, the server accepts the submission of sets of proteins either by uploading the files or programmatically by using command line WSDL API scripts. This makes SubCons ideal for proteome wide analyses allowing the user to scan a whole proteome in few days. From the web page, it is also possible to download precalculated predictions for several eukaryotic organisms. To evaluate the performance of SubCons we present a benchmark of LocTree3 and SubCons using two recent mass-spectrometry based datasets of mouse and drosophila proteins. The server is available at http://subcons.bioinfo.se/

  • 9.
    Salvatore, Marco
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Warholm, Per
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Shu, Nanjiang
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Basile, Walter
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    Elofsson, Arne
    Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).
    SubCons: a new ensemble method for improved human subcellular localization predictions2017Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, nr 16, s. 2464-2470Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Motivation: Knowledge of the correct protein subcellular localization is necessary for understanding the function of a protein. Unfortunately large-scale experimental studies are limited in their accuracy. Therefore, the development of prediction methods has been limited by the amount of accurate experimental data. However, recently large-scale experimental studies have provided new data that can be used to evaluate the accuracy of subcellular predictions in human cells. Using this data we examined the performance of state of the art methods and developed SubCons, an ensemble method that combines four predictors using a Random Forest classifier. Results: SubCons outperforms earlier methods in a dataset of proteins where two independent methods confirm the subcellular localization. Given nine subcellular localizations, SubCons achieves an F1-Score of 0.79 compared to 0.70 of the second bestmethod. Furthermore, at a FPR of 1% the true positive rate (TPR) is over 58% for SubCons compared to less than 50% for the best individual predictor.

    Fulltekst (pdf)
    fulltext
1 - 9 of 9
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf