Allmer, Jens

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Profile URL
https://hdl.handle.net/20.500.14365/7345
Name Variants
Job Title
Email Address
jens.allmer@ieu.edu.tr
Main Affiliation
05.09. Industrial Engineering
Status
Former Staff
Website
ORCID ID
0000-0002-2164-7335
Scopus Author ID
24821311300
Turkish CoHE Profile ID
Google Scholar ID
BHfsiQEAAAAJ
WoS Researcher ID
DWU-1027-2022

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86

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3600

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2

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2

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WoS Citation Count

2145

Scopus Citation Count

2272

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2

Scopus h-index

2

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0

WoS Citations per Publication

1,072.50

Scopus Citations per Publication

1,136.00

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2

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0

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  • Thumbnail Image
    Article
    Citation - WoS: 2139
    Citation - Scopus: 2265
    The Chlamydomonas Genome Reveals the Evolution of Key Animal and Plant Functions
    (Amer Assoc Advancement Science, 2007) Merchant, Sabeeha S.; Prochnik, Simon E.; Vallon, Olivier; Harris, Elizabeth H.; Karpowicz, Steven J.; Witman, George B.; Terry, Astrid; Allmer, Jens
    Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the similar to 120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.
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    Article
    Citation - WoS: 6
    Citation - Scopus: 7
    2db: a Proteomics Database for Storage, Analysis, Presentation, and Retrieval of Information From Mass Spectrometric Experiments
    (Biomed Central Ltd, 2008) Allmer, Jens; Kuhlgert, Sebastian; Hippler, Michael
    Background: The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. Results: In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. Conclusion: We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.