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Projects / Programmes source: ARIS

Application of single cell sequencing and machine learning in mammary gland biology

Research activity

Code Science Field Subfield
4.06.00  Biotechnical sciences  Biotechnology   

Code Science Field
4.04  Agricultural and Veterinary Sciences  Agricultural biotechnology 
Keywords
epigenetics, machine learning, lactation, mammary gland, single cell sequencing, tanscriptome
Evaluation (rules)
source: COBISS
Points
8,817.97
A''
1,756.73
A'
3,926.07
A1/2
5,240.58
CI10
11,784
CImax
628
h10
46
A1
29.13
A3
6.24
Data for the last 5 years (citations for the last 10 years) on April 20, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  535  13,251  11,689  21.85 
Scopus  650  17,476  15,458  23.78 
Researchers (20)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  53798  Jure Brence  Computer science and informatics  Researcher  2021 - 2024  21 
2.  36220  PhD Martin Breskvar  Computer science and informatics  Researcher  2021 - 2023  36 
3.  55967  Mateja Dolinar  Animal production  Researcher  2022 - 2024  17 
4.  05098  PhD Peter Dovč  Biotechnology  Head  2021 - 2024  935 
5.  11130  PhD Sašo Džeroski  Computer science and informatics  Researcher  2021 - 2024  1,204 
6.  56464  Tamara Ferme  Animal production  Technical associate  2022  46 
7.  57060  Boštjan Gec  Computer science and informatics  Researcher  2022 - 2024 
8.  55951  Kaja Kajtna    Technical associate  2022 - 2024 
9.  08405  PhD Marija Klopčič  Animal production  Researcher  2021 - 2024  721 
10.  31050  PhD Dragi Kocev  Computer science and informatics  Researcher  2021 - 2024  204 
11.  35470  PhD Jurica Levatić  Computer science and informatics  Researcher  2022 - 2023  43 
12.  05008  PhD Mojca Narat  Biotechnology  Researcher  2021 - 2024  688 
13.  28505  PhD Jernej Ogorevc  Animal production  Researcher  2021 - 2024  137 
14.  27759  PhD Panče Panov  Computer science and informatics  Researcher  2021 - 2024  155 
15.  38206  PhD Matej Petković  Computer science and informatics  Researcher  2021 - 2023  65 
16.  34333  PhD Tine Pokorn  Plant production  Researcher  2021  38 
17.  57192  Sintija Stevanoska  Computer science and informatics  Researcher  2022 - 2024 
18.  39597  PhD Jovan Tanevski  Computer science and informatics  Researcher  2021 - 2024  33 
19.  55503  Anja Tanšek  Animal production  Technical associate  2022 - 2024  21 
20.  32581  PhD Minja Zorc  Computer science and informatics  Researcher  2021 - 2024  182 
Organisations (2)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0481  University of Ljubljana, Biotechnical Faculty  Ljubljana  1626914  66,295 
2.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,682 
Abstract
The mammary gland is a highly specialised organ in mammals, which has an extremely important role in reproduction and is essential for economical milk production in agriculture. The capacity for milk production in dairy cows exceeds several times the nutritional needs of the calf and represents a unique production trait, which has been efficiently improved using the classical selection approach. Therefore, lactation in cattle is an ideal model for studying the biology of lactation with the aim to discover the mechanistic base of this complex trait at cellular level with the potential to contribute to the basic knowledge about lactation biology. Recently, it has become possible to investigate the single cell transcriptomes instead of bulk RNA pools from different cell types. Since then, mammary epithelial cells at the single-cell level in humans and mice were examined and revealed much higher heterogeneity of the mammary epithelial cell population than previously reported. To date, no experiment has been carried out to produce the profile of bovine mammary gland gene expression using the single cell RNA sequencing (scRNA-Seq) approach, nor has milk transcriptome been profiled at the single cell level. Within the frame of this project, scRNA-Seq will be applied to document cell type specific expression profiles in the mammary gland and to determine different cell types based on cell type specific transcription profile. This approach will allow us to identify cellular sources for several milk components, which did not have a defined origin before. Further analysis of transcriptomic data will allow for the identification of regulatory elements (transcription factors, prediction of binding sites for mammary gland expressed transcription factors, etc.). With the application of inter species comparison of transcriptomic profiles we will try to identify generally and species -specific expressed genes. Single cell transcriptomics data characterizes living systems at an unprecedented level of resolution, however, it is extremely sparse and noisy. As scRNA-seq data has potential to reveal novel insights into complex biological systems, it also poses several new algorithmic challenges. We will apply machine learning methods to address the problems related to scRNA-Seq data analysis and integration of transcriptomic data with the chromatin structure data provided by scATAC-Seq.
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