TeMMPo is a Django web application that expect a file of abstracts representing the articles linking exposure to outcome. It then expects you to specify MeSH terms for the exposure, MeSH terms for candidate mechanisms and MeSH terms for outcomes. This page describes each of these in more detail.
You must create an account to use TeMMPo. TeMMPo is provided as a free-to-use service, and the user registration and login is simply used to enable you to track jobs and re-use search criteria.
The article upload page is the first link under "Search" after you have logged in to TeMMPo.
You can upload abstracts in either OVID MEDLINE® or PubMed MEDLINE® format. The following examples illustrate the required format. It is possible to exclude some fields to reduce file size, but it is essential that MeSH Subject Headings are included. At present the file upload limit is set at 2000MB.
‐ Includes "MeSH Subject Headings"
<1>
Unique Identifier
23482392
Record Owner
From MEDLINE, a database of the U.S. National Library of Medicine.
Status
MEDLINE
Authors
Li C. Han J. Yao Q. Zou C. Xu Y. Zhang C. Shang D. Zhou L. Zou C. Sun Z. Li J. Zhang Y. Yang H. Gao X. Li X.
Authors Full Name
Li, Chunquan. Han, Junwei. Yao, Qianlan. Zou, Chendan. Xu, Yanjun. Zhang, Chunlong. Shang, Desi. Zhou, Lingyun. Zou, Chaoxia. Sun, Zeguo. Li, Jing. Zhang, Yunpeng. Yang, Haixiu. Gao, Xu. Li, Xia.
Institution
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Title
Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways.
Source
Nucleic Acids Research. 41(9):e101, 2013 May.
Other ID
Source: NLM. PMC3643575
MeSH Subject Headings
Colorectal Neoplasms/ge [Genetics]
Colorectal Neoplasms/me [Metabolism]
Histamine/me [Metabolism]
Humans
Male
*Metabolic Networks and Pathways/ge [Genetics]
*Metabolomics
Neoplasm Metastasis
Prostatic Neoplasms/ge [Genetics]
Prostatic Neoplasms/me [Metabolism]
Prostatic Neoplasms/pa [Pathology]
*Transcriptome
Abstract
Various 'omics' technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.
Registry Number/Name of Substance
51-45-6 (Histamine).
Publication Type
Journal Article. Research Support, Non-U.S. Gov't.
Date Created
20130506
Year of Publication
2013
Link to the Ovid Full Text or citation
http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&D=medl&AN=23482392
Link to the External Link Resolver
http://linkserver.bristol.ac.uk:9003/prod?sid=OVID:medline&id=pmid:23482392&id=doi:10.1093%2Fnar%2Fgkt161&issn=0305-1048&isbn=&volume=41&issue=9&spage=e101&pages=e101&date=2013&title=Nucleic+Acids+Research&atitle=Subpathway-GM%3A+identification+of+metabolic+subpathways+via+joint+power+of+interesting+genes+and+metabolites+and+their+topologies+within+pathways.&aulast=Li&pid=%3Cauthor%3ELi+C%3C%2Fauthor%3E%3CAN%3E23482392%3C%2FAN%3E%3CDT%3EJournal+Article%3C%2FDT%3E
‐ Includes "MH"
PMID- 26010633
OWN - NLM
STAT- MEDLINE
DA - 20150527
DCOM- 20150529
LR - 20150708
IS - 1538-3598 (Electronic)
IS - 0098-7484 (Linking)
VI - 313
IP - 20
DP - 2015 May 26
TI - Copy number variations and cognitive phenotypes in unselected populations.
PG - 2044-54
LID - 10.1001/jama.2015.4845 [doi]
AB - IMPORTANCE: The association of copy number variations (CNVs), differing numbers
of copies of genetic sequence at locations in the genome, with phenotypes such as
intellectual disability has been almost exclusively evaluated using clinically
ascertained cohorts. The contribution of these genetic variants to cognitive
phenotypes in the general population remains unclear. OBJECTIVE: To investigate
the clinical features conferred by CNVs associated with known syndromes in adult
carriers without clinical preselection and to assess the genome-wide consequences
of rare CNVs (frequency </=0.05%; size >/=250 kilobase pairs [kb]) on carriers'
educational attainment and intellectual disability prevalence in the general
population. DESIGN, SETTING, AND PARTICIPANTS: The population biobank of Estonia
contains 52,000 participants enrolled from 2002 through 2010. General
practitioners examined participants and filled out a questionnaire of health- and
lifestyle-related questions, as well as reported diagnoses. Copy number variant
analysis was conducted on a random sample of 7877 individuals and
genotype-phenotype associations with education and disease traits were evaluated.
Our results were replicated on a high-functioning group of 993 Estonians and 3
geographically distinct populations in the United Kingdom, the United States, and
Italy. MAIN OUTCOMES AND MEASURES: Phenotypes of genomic disorders in the general
population, prevalence of autosomal CNVs, and association of these variants with
educational attainment (from less than primary school through scientific degree)
and prevalence of intellectual disability. RESULTS: Of the 7877 in the Estonian
cohort, we identified 56 carriers of CNVs associated with known syndromes. Their
phenotypes, including cognitive and psychiatric problems, epilepsy, neuropathies,
obesity, and congenital malformations are similar to those described for carriers
of identical rearrangements ascertained in clinical cohorts. A genome-wide
evaluation of rare autosomal CNVs (frequency, </=0.05%; >/=250 kb) identified 831
carriers (10.5%) of the screened general population. Eleven of 216 (5.1%)
carriers of a deletion of at least 250 kb (odds ratio [OR], 3.16; 95% CI,
1.51-5.98; P = 1.5e-03) and 6 of 102 (5.9%) carriers of a duplication of at least
1 Mb (OR, 3.67; 95% CI, 1.29-8.54; P = .008) had an intellectual disability
compared with 114 of 6819 (1.7%) in the Estonian cohort. The mean education
attainment was 3.81 (P = 1.06e-04) among 248 (>/=250 kb) deletion carriers and
3.69 (P = 5.024e-05) among 115 duplication carriers (>/=1 Mb). Of the deletion
carriers, 33.5% did not graduate from high school (OR, 1.48; 95% CI, 1.12-1.95; P
= .005) and 39.1% of duplication carriers did not graduate high school (OR, 1.89;
95% CI, 1.27-2.8; P = 1.6e-03). Evidence for an association between rare CNVs and
lower educational attainment was supported by analyses of cohorts of adults from
Italy and the United States and adolescents from the United Kingdom. CONCLUSIONS
AND RELEVANCE: Known pathogenic CNVs in unselected, but assumed to be healthy,
adult populations may be associated with unrecognized clinical sequelae.
Additionally, individually rare but collectively common intermediate-size CNVs
may be negatively associated with educational attainment. Replication of these
findings in additional population groups is warranted given the potential
implications of this observation for genomics research, clinical care, and public
health.
FAU - Mannik, Katrin
AU - Mannik K
AD - Center for Integrative Genomics, University of Lausanne, Lausanne,
Switzerland2Estonian Genome Center, University of Tartu, Tartu.
FAU - Magi, Reedik
AU - Magi R
AD - Estonian Genome Center, University of Tartu, Tartu.
FAU - Mace, Aurelien
AU - Mace A
AD - Department of Medical Genetics, University of Lausanne, Lausanne,
Switzerland4Swiss Institute of Bioinformatics, Lausanne, Switzerland.
FAU - Cole, Ben
AU - Cole B
AD - Department of Laboratory Medicine and Pathology, University of Minnesota Medical
School, Minneapolis.
FAU - Guyatt, Anna L
AU - Guyatt AL
AD - Bristol Genetic Epidemiology Laboratories, School of Social and Community
Medicine, University of Bristol, Bristol, United Kingdom.
FAU - Shihab, Hashem A
AU - Shihab HA
AD - Bristol Genetic Epidemiology Laboratories, School of Social and Community
Medicine, University of Bristol, Bristol, United Kingdom7MRC Integrative
Epidemiology Unit, School of Social and Community Medicine, University of
Bristol, Bristol, United Kingdom.
FAU - Maillard, Anne M
AU - Maillard AM
AD - Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.
FAU - Alavere, Helene
AU - Alavere H
AD - Estonian Genome Center, University of Tartu, Tartu.
FAU - Kolk, Anneli
AU - Kolk A
AD - Estonian Genome Center, University of Tartu, Tartu8Department of Neurology and
Neurorehabilitation, Children's Clinic, Tartu University Hospital, Tartu,
Estonia.
FAU - Reigo, Anu
AU - Reigo A
AD - Estonian Genome Center, University of Tartu, Tartu.
FAU - Mihailov, Evelin
AU - Mihailov E
AD - Estonian Genome Center, University of Tartu, Tartu.
FAU - Leitsalu, Liis
AU - Leitsalu L
AD - Estonian Genome Center, University of Tartu, Tartu9Institute of Molecular and
Cell Biology, University of Tartu, Tartu, Estonia.
FAU - Ferreira, Anne-Maud
AU - Ferreira AM
AD - Center for Integrative Genomics, University of Lausanne, Lausanne,
Switzerland4Swiss Institute of Bioinformatics, Lausanne, Switzerland.
FAU - Noukas, Margit
AU - Noukas M
AD - Estonian Genome Center, University of Tartu, Tartu9Institute of Molecular and
Cell Biology, University of Tartu, Tartu, Estonia.
FAU - Teumer, Alexander
AU - Teumer A
AD - Institute for Community Medicine, University Medicine Greifswald, Greifswald,
Germany.
FAU - Salvi, Erika
AU - Salvi E
AD - Deparment of Health Sciences, University of Milan, Milan, Italy.
FAU - Cusi, Daniele
AU - Cusi D
AD - Deparment of Health Sciences, University of Milan, Milan, Italy12Institute of
Biomedical Technologies, Italian National Research Council, Milan, Italy.
FAU - McGue, Matt
AU - McGue M
AD - Department of Psychology, University of Minnesota, Minneapolis.
FAU - Iacono, William G
AU - Iacono WG
AD - Department of Psychology, University of Minnesota, Minneapolis.
FAU - Gaunt, Tom R
AU - Gaunt TR
AD - Bristol Genetic Epidemiology Laboratories, School of Social and Community
Medicine, University of Bristol, Bristol, United Kingdom7MRC Integrative
Epidemiology Unit, School of Social and Community Medicine, University of
Bristol, Bristol, United Kingdom.
FAU - Beckmann, Jacques S
AU - Beckmann JS
AD - Swiss Institute of Bioinformatics, Lausanne, Switzerland.
FAU - Jacquemont, Sebastien
AU - Jacquemont S
AD - Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.
FAU - Kutalik, Zoltan
AU - Kutalik Z
AD - Department of Medical Genetics, University of Lausanne, Lausanne,
Switzerland4Swiss Institute of Bioinformatics, Lausanne, Switzerland14Institute
of Social and Preventive Medicine, Lausanne University Hospital (CHUV), Lausanne,
Switzerland.
FAU - Pankratz, Nathan
AU - Pankratz N
AD - Department of Laboratory Medicine and Pathology, University of Minnesota Medical
School, Minneapolis.
FAU - Timpson, Nicholas
AU - Timpson N
AD - Bristol Genetic Epidemiology Laboratories, School of Social and Community
Medicine, University of Bristol, Bristol, United Kingdom7MRC Integrative
Epidemiology Unit, School of Social and Community Medicine, University of
Bristol, Bristol, United Kingdom.
FAU - Metspalu, Andres
AU - Metspalu A
AD - Estonian Genome Center, University of Tartu, Tartu9Institute of Molecular and
Cell Biology, University of Tartu, Tartu, Estonia.
FAU - Reymond, Alexandre
AU - Reymond A
AD - Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
LA - eng
GR - 102433/Z/13/Z/Wellcome Trust/United Kingdom
GR - AA09367/AA/NIAAA NIH HHS/United States
GR - AA11886/AA/NIAAA NIH HHS/United States
GR - DA024417/DA/NIDA NIH HHS/United States
GR - DA05147/DA/NIDA NIH HHS/United States
GR - DA13240/DA/NIDA NIH HHS/United States
GR - MH066140/MH/NIMH NIH HHS/United States
PT - Journal Article
PT - Research Support, N.I.H., Extramural
PT - Research Support, Non-U.S. Gov't
PL - United States
TA - JAMA
JT - JAMA
JID - 7501160
SB - AIM
SB - IM
CIN - JAMA. 2015 May 26;313(20):2029-30. PMID: 26010630
MH - Adolescent
MH - Adult
MH - Cognition
MH - *DNA Copy Number Variations
MH - Educational Status
MH - Epilepsy/genetics
MH - Estonia
MH - Female
MH - Genome-Wide Association Study
MH - Great Britain
MH - *Heterozygote
MH - Humans
MH - Intellectual Disability/*genetics
MH - Italy
MH - Male
MH - Mental Disorders/*genetics
MH - Obesity/genetics
MH - Phenotype
MH - United States
EDAT- 2015/05/27 06:00
MHDA- 2015/05/30 06:00
CRDT- 2015/05/27 06:00
AID - 2297168 [pii]
AID - 10.1001/jama.2015.4845 [doi]
PST - ppublish
SO - JAMA. 2015 May 26;313(20):2044-54. doi: 10.1001/jama.2015.4845.
After uploading your abstracts you will be able to enter exposure terms. There are three options for selecting terms:
This can be useful for editing and reusing long lists of MeSH terms
You can choose to "explode" descendent MeSH terms with the radio buttons at the top of the list
This automatically expands the relevant branches, allowing you to easily find and select terms
After selecting exposures you will be able to enter mechanism terms. The same three options for selecting terms are presented:
This can be useful for editing and reusing long lists of MeSH terms
You can choose to "explode" descendent MeSH terms with the radio buttons at the top of the list
This automatically expands the relevant branches, allowing you to easily find and select terms
You can use the "breadcrumb trail" at the top to browse back to exposure selection if you wish
Finally, after entering mechanism MeSH terms you will be able to select outcome terms. There are three options for selecting terms:
This can be useful for editing and reusing long lists of MeSH terms
You can choose to "explode" descendent MeSH terms with the radio buttons at the top of the list
This automatically expands the relevant branches, allowing you to easily find and select terms
You can use the "breadcrumb trail" at the top to browse back to exposure or mechanism selection if you wish
The genes and filter section lets you enter gene names (or any text words) as potential mechanistic terms. This expands the functionality of TeMMPo to enable mechanistic terms other than MeSH terms - this is particularly relevant for terms such as gene names (which are not represented in MeSH).
An additional overall MeSH filter can also be applied in this section. This enables the user to restrict the articles used in the counting process.
Once your analysis is complete you are presented with a table of results. If this is your first analysis only one row will show. The results table contains:
| Abstract file | Date | Status | View search criteria | Mechanism visualisations | Score visualisations | Mechanism match counts | Score data [help] | Mechanism Abstract IDs [help] | Delete |
|---|
How is the priority score calculated?
The priority score is a weighted ratio of the number of abstracts linking the mechanisms to exposure (EM) and outcome (MO), calculated as:
score = max(EM,MO) / min(EM,MO) x (EM + MO)
Select "Search Criteria" from the results table, or selecting "Reuse existing search" from the "Search" menu enables you to reuse either a set of abstracts you have uploaded previously or a set of search criteria (or both).
Reuse search criteria will repeat a previous analysis (on the same set of abstracts), but allowing you to edit all of the MeSH terms used.
Reuse abstracts only will allow you to define an entirely new search on an existing set of articles.
A CHANGELOG is published on GitHub.
In May 2019 bugs were found in the results sorting of visualisations used on this website and fixes were released.
In July 2020 changes were made to resolve an issue that resulted in under matching genes and also mechanisms that included symbols. Also support was added for case insenstive and sub heading MeSH terms matching.
- denotes a result where revisions to the matching algorithm have resulted in changes to the original calculation.