MPTM: A tool for mining protein post-translational modifications from literature

Sun, Dongdong; Wang, Minghui; Li, Ao

doi:10.1142/s0219720017400054

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…The Spd-2 phosphorylation sites we identified here were also identified previously in at least one of several phospho-proteomic screens in D. melanogaster (Bodenmiller et al, 2007; Habermann et al, 2012; Hu et al, 2019; Zhai et al, 2008). These screens, however, also identified many additional phosphorylated peptides in Spd-2, including 16 peptides that contained at least one phosphorylated S-S/T(p) motif that could potentially bind the PBD (Table 2).…”

Section: Resultssupporting

confidence: 78%

Evidence that a positive feedback loop drives centrosome maturation in fly embryos

Alvarez‐Rodrigo

Steinacker

Saurya

et al. 2019

eLife

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Centrosomes are formed when mother centrioles recruit pericentriolar material (PCM) around themselves. The PCM expands dramatically as cells prepare to enter mitosis (a process termed centrosome maturation), but it is unclear how this expansion is achieved. In flies, Spd-2 and Cnn are thought to form a scaffold around the mother centriole that recruits other components of the mitotic PCM, and the Polo-dependent phosphorylation of Cnn at the centrosome is crucial for scaffold assembly. Here, we show that, like Cnn, Spd-2 is specifically phosphorylated at centrosomes. This phosphorylation appears to create multiple phosphorylated S-S/T(p) motifs that allow Spd-2 to recruit Polo to the expanding scaffold. If the ability of Spd-2 to recruit Polo is impaired, the scaffold is initially assembled around the mother centriole, but it cannot expand outwards, and centrosome maturation fails. Our findings suggest that interactions between Spd-2, Polo and Cnn form a positive feedback loop that drives the dramatic expansion of the mitotic PCM in fly embryos.

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Section: Resultssupporting

confidence: 78%

Evidence that a positive feedback loop drives centrosome maturation in fly embryos

Alvarez‐Rodrigo

Steinacker

Saurya

et al. 2019

eLife

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“…Most of the current text mining tools are either online, like PESCADOR [ 9 ], STRING [ 14 – 16 ] and PPIcurator [ 49 ]; or very specific, such as FamPlex [ 50 ], for human proteins, MPTM [ 51 ], for post-translation modification in humans, and PaperBLAST [ 52 ], for homology search. LAITOR4HPC and STRING updates are the only programmatic text mining tools available that came to our knowledge.…”

Section: Discussionmentioning

confidence: 99%

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

et al. 2020

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Background: The amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR's native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied. Results: By taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 cooccurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response. Conclusions: LAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.

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“…from clinical records (Gupta et al, 2018), relation extraction, e.g. identifying protein post-translational modifications (Sun et al, 2017), and used as features for machine learning tasks, e.g. gene mention detection (Smith and Wilbur, 2009), among other uses.…”

Section: Introductionmentioning

confidence: 99%

CRAFT Shared Tasks 2019 Overview –- Integrated Structure, Semantics, and Coreference

Baumgartner¹,

Bada²,

Pyysalo³

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks -dependency parse construction, coreference resolution, and ontology concept identification -over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.

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MPTM: A tool for mining protein post-translational modifications from literature

Cited by 8 publications

References 40 publications

Evidence that a positive feedback loop drives centrosome maturation in fly embryos

Evidence that a positive feedback loop drives centrosome maturation in fly embryos

LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks

CRAFT Shared Tasks 2019 Overview –- Integrated Structure, Semantics, and Coreference

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