Bioinformatic tools

On this page we will present a collection of our in-home, free available bioinformatic tools and databases. Please remember to cite the appropriate publication when using our tools. Thank You!

Literature-based, manually-curated database of PCR primers


A literature-based, manually-curated database of PCR primers for the detection of antibiotic resistance genes is comprised of hundreds of PCR primer pairs designed for the amplification of various genes conferring resistance to antibiotics. Three parameters were assigned for each primer pair: specificity (S), efficacy (E) and taxonomic efficacy (TE). These parameters were evaluated using a novel bioinformatic tool – UniPriVal – that was used to validate each primer pair against various reference databases. Then, primer pairs specific for each gene were ranked based on their model success metric (MSM) value. It is important to mention, that due to the correlation between E and TE parameters, the MSM metric is biased toward primer pairs with higher E/TE values relative to S values. Despite this limitation, the internal validation system of the LCPDb application enables the quantified ranking of PCR primer pairs, which assists selection of the best primers for each application. At the end we would like to add that, although we performed thorough literature review to identify PCR primers, we are aware that the database is still incomplete and needs further development. Therefore, users are invited to directly contact (using the “Submit” webpage) the database developers to add missing primers as well as to test their own primer pairs with the UniPriVal tool.

Last update: 2019.06.19
Reference: Gorecki, A., Decewicz, P., Dziurzynski, M., Janeczko, A., Drewniak, L., & Dziewit, L. (2019). Literature-based, manually-curated database of PCR primers for the detection of antibiotic resistance genes in various environments. Water Research, 161, 211-221. doi:10.1016/j.watres.2019.06.009. Read me.

Alphaproteobacteria (pro)phages Database


This projects aims to construct interactive website database for all known and publicly available active phages and manually verified and reannotated prophages within Alphaproteobacteria.

Alphaproteobacteria as a taxonomy clade is well represented by bacteria inhabiting various environments and thus they are metabolicly diverse. Within, there are phototrophic (i.e. Rhodobacter), endosymbiotic (i.e. Wolbachia), metylothrophic (i.e. Paracoccus) and parasitic (i.e. Rickettsia) bacteria. These are also commonly used in biotechnology like nitrogen-fixing rhyzobia, in biodeterioration – the species degrading and denitrifing polluted soils or molecular biology – Agrobacterium tumefaciens which is able to insert exogenous DNA to plant cells. They are also diverse within the genome structure as the range of genome size is between 140 kb to 9000 kb. The number and sizes of replicons also varies – from none to even 9. Same with %GC content: 27.5 – 71.5%.

Once published, the database site link will be attached here. Right know, in Download section you may find raw data that were used to construct the database or has already been published (e.g. (pro)phages infecting Sinorhizobium spp. and Paracoccus spp.).

Last update: 2019.05.20
– Decewicz P., Radlinska M., Dziewit L. 2017. Characterization of Sinorhizobium sp. LM21 prophages and virus-encoded DNA methyltransferases in the light of comparative genomic analyses of the sinorhizobial virome. Viruses 9: 161. Read me
– Decewicz P., Dziewit L., Golec P., Kozlowska P., Bartosik D., Radlinska M. 2019. Characterization of the virome of Paracoccus spp. (Alphaproteobacteria) by combined in silico and in vivo approaches. Scientific Reports 9: 7899. Read me.

Stress response genes database



A manually curated database of stress response genes was created to annotate genes of plasmid DNA extracted from metaplasmidomes from polar environments. Based on a literature review, genes putatively involved in adaptation to cold environments, with a focus on the response to changing environmental conditions, were identified. We named selected genes according to Uniprot and NCBI databases. If available, references were added. Next, a collection of antibiotic resistance genes (ARGs) that had originally been extracted from the comprehensive antibiotic resistance gene database (CARD) (Jia et al., 2017) and the heavy-metal resistance gene database (BacMet) (Pal et al. 2014) was added. The compiled  database contains 2,451 sequences of proteins that putatively encode stress-response genes, including (i) 2,191 antibiotic-resistant genes and proteins, (ii) 119 heavy-metal resistant genes, (iii) 49 reactive oxygen species protection genes, (iv) 30 UV radiation protection genes, (v) 23 cold shock genes, (vi) 13 anti-freeze genes, (vii) 13 osmoregulatory genes, (viii) 4 phasins, (ix) 3 trehalose synthetases, (x) 3 hydroxyalkanoic acid synthetases, and (xi) 3 ice nucleation genes. In the end, we would like to add that, although we performed a thorough literature review to identify the stress response genes, we are aware that the database is still incomplete and needs further development.

Last update: 2020.03.24