As I wrote a few days ago, I have now started my new position at Chalmers SysBio. This position is funded by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), which also funds PhD and postdoc positions. We are now announcing two doctoral student projects and one postdoc project within the DDLS program in my lab.
Common to all projects is that they will the use of large-scale data-driven approaches (including machine learning and (meta)genomic sequence analysis), high-throughput molecular methods and established theories developed for macro-organism ecology to understand biological phenomena. We are for all three positions looking for people with a background in bioinformatics, computational biology or programming. In all three cases, there will be at least some degree of analysis and interpretation of large-scale data from ongoing and future experiments and studies performed by the group and our collaborators. The positions are all part of the SciLifeLab national research school on data-driven life science, which the students and postdoc will be expected to actively participate in.
The postdoc and one of the doctoral students are expected to be involved in a project aiming to uncover interactions between the bacteria in microbiomes that are important for community stability and resilience to being colonized by pathogens. This project also seeks to unearth which environmental and genetic factors that are important determinants of bacterial invasiveness and community stability. The project tasks may include things like predicting genes involved in pathogenicity and other interactions from sequencing data, and performing large-scale screening for such genes in microbiomes.
The second doctoral student is expected to work in a project dealing with understanding and limiting the spread of antibiotic resistance through the environment, identifying genes involved in antibiotic resistance, defining the conditions that select for antibiotic resistance in different settings, and developing approaches for monitoring for antibiotic resistance in the environment. Specifically, the tasks involved in this project may be things like identifying risk environments for AMR, define potential novel antibiotic resistance genes, and building a platform for AMR monitoring data.
For all these three positions, there is some room for adapting the specific tasks of the projects to the background and requests of the recruited persons!
We are very excited to see your applications and to jointly build the next generation of data driven life scientist! Read more about the positions here.
Together with Joakim Larsson‘s lab, we now have an open two-year postdoc position in bioinformatics on antibiotic resistance and biocide resistance. The development of antibiotic resistance has been driven by use of antibiotics, but antibacterial biocides also have the potential to select for antibiotic resistance. However, knowledge of which genes that contribute to biocide resistance and could be associated with antibiotic resistance is sparse. To some extent, such genes are documented in the BacMet database which we have developed, but this collection of resistance genes is only scratching the surface of all biocide resistance that exists among bacteria in the environment.
We are now looking for a postdoctoral fellow to continue the important work on bioinformatic analysis of biocide and antibiotic resistance to answer the question whether increasing biocide resistance would be a threat to human health. The postdoc will be working with the development of the BacMet database to make it more targeted towards biocidal substances and products in addition to resistance genes. The tasks include bioinformatic sequence analysis, literature studies and database and web programming. The work will also include investigations of the prevalence of the identified resistance genes in genomes and metagenomes.
The recruited person will work closely with both my group and the group of Prof. Joakim Larsson, and will participate in the JPIAMR-funded BIOCIDE project. You can apply to the postdoc position at the University of Gothenburg application portal: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89bead79bb7258ad55c8d75228e5b7&job_id=25122
The deadline is May 4, 2022. Come work with us on this exciting topic in the intersect between two great research environments (if I may say it myself!) We look forward to your application!
I have very big and exciting news to share with you. After more than 10 years at the Sahlgrenska Academy, me and my lab will be moving from the University of Gothenburg to Chalmers University of Technology (which is physically a move of less than a kilometer, so still within Gothenburg). I have been offered a position at the Division of Systems Biology, funded by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS). The total funding to my lab will be 17 million SEK, with some co-funding from Chalmers added in on top of that.
I am of course very excited about this opportunity, which will bring some infrastructure that we need in-house that we don’t have easy access to today. At the same time, I am sad to leave my academic ‘home’, and the fantastic people we have been working with there for the years. I am also endlessly thankful for the support and trust that the Sahlgrenska Academy, the Institute of Biomedicine and the Department of Infectious Diseases have put into me and my research over the past years.
The transition to Chalmers will start already in May, but will be gradual and continue for a long time. We have close ties to the Sahlgrenska Academy and we will keep closely collaborating with researchers there. I will also retain an affiliation to the University of Gothenburg, at least for the near future.
All in all, this year will bring very interesting development, and this additional funding from the DDLS program will allow us to venture into new areas of bioinformatics and try out ideas that have previously been out of reach. I look forward to work with our new colleagues at Chalmers and within the DDLS program in the coming years!
If you are skilled in bioinformatics and want to work with one of my favorite persons, you should check out this postdoc ad closing January 9. This two-year position in Erik Kristiansson‘s lab at Chalmers University of Technology is a great opportunity to work with fantastic people on highly interesting questions. It has applications in infectious diseases and antibiotic resistance, and will be focused on genomic analysis of antibiotic resistance and virulence and their evolutionary history. The work includes both the development of new data-driven methodologies and the application of existing methodology to new datasets. The position will involve collaborations with researchers from clinical microbiology and the environmental sciences within the Centre for Antibiotic Resistance Research.
We are hiring a PhD student to work with interactions between the bacteria in human and environmental microbiomes that are important for community stability and resilience to being colonized by unwanted bacteria (including pathogens). The project seeks to unearth which environmental and genetic factors that are important determinants of bacterial invasiveness and community stability. You can read more at our Open Positions page.
We are looking for a candidate with experience with both bioinformatics and experimental microbiology. Previous experience with microbial communities is a plus, but not a must, as is work with human cell lines.
The project is fully funded by a grant from the Swedish Research Council and the position is planned for 4.5 years, with 4 years of research and course work and half a year of teaching.
If you feel that you are the right person for this position, you can apply here. We look forward to your application! The deadline for applications is October 21.
Last week, a preprint describing a study which I have played a small part in was posted on bioRxiv. This paper (1) uses the Metaxa2 database (2) to tease out how much of an effect mitochondrial rRNA sequences have on studies of bacterial diversity in corals. And it turns out that it matters… a lot. Importantly, by supplementing the taxonomic databases with diverse mitochondrial rRNA sequences from the Metaxa2 database, ~97% of unique unclassified sequences could be resolved as mitochondrial, without increasing the level of misannotation in mock communities. Thus the study not only points to a problem, but also to its solution! You can read it all here.
- Sonnet D, Brown T, Bengtsson-Palme J, Padilla-Gamiño J, Zaneveld JR: The Organelle in the Room: Under-annotated Mitochondrial Reads Bias Coral Microbiome Analysis. bioRxiv, 431501 (2021). doi: 10.1101/2021.02.23.431501 [Link]
- Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]
Yes, Saturdays are somewhat weird days for software updates, but if you’re doing weekend work anyway, why wait to push bug fixes to the community? A very minor bug-fix update to Metaxa2 was released today, bringing the software to version 2.2.3.
Two things have changed in this version, both related to the genome mode. 1) We fixed a file reading bug in the ‘genome’ mode of the software. This bug caused the last sequence in an input FASTA file not to be read unless there was a newline after it. Since the ‘genome’ mode is rarely used by most users, we suspect not a lot of users have been affected by this bug.
2) While we were at it, we changed the behavior of the ‘genome’ mode to mirror that of the ‘auto’ mode, as the strict genome mode dropped sequences shorter than 2500 bp. We considered this behavior counter-intuitive to what most users would want, and has now changed the ‘genome’ mode to behave the same as the ‘auto’ mode and not drop short sequences.
No other changes have been made in this version. The update can be found at the Metaxa2 software page.
A new version of ITSx is released today. This minor update contains two minor bug fixes and two small new features.
The first bug was that ITSx returned empty sequences in the FASTA file for no detections for large input files. This has now been fixed.
The second bug fix is a bit more fuzzy and involved some fine-tuning of how large input files are handled in ITSx to stabilise E-value and score cut-offs.
The two new features are:
- The possbility to put the temporary directory in a custom location using the
- ITSx now warns when the input file contains sequences with identical identifiers, which usually leads to sequences being dropped from the input file.
The new update brings ITSx to version 1.1.3. Thanks for the users who have spotted bugs and suggested new features! Happy barcoding everyone!
We start the new year with a bang, or at least a new paper published. Bioinformatics put our paper (1) describing the software package CAFE online today (although it was accepted late last year). The CAFE package is a combination of Perl and R tools that can analyze data from paired transposon mutant sequencing experiments (2-4), generate fitness coefficients for each gene and condition, and perform appropriate statistical testing on these fitness coefficients. The paper is short, but shows that CAFE performs as good as the best competing tools (5-7) while being superior at controlling for false positives (you’ll have to dig into the supplement to find the data for that though).
Importantly, this is a collaborative effort by basically the entire research group from last spring: me, Haveela, Emil, Anna and our visiting student Adriana. A big thanks to all of you for working on this short but important paper! You can read the full paper here.
- Abramova A, Osińska A, Kunche H, Burman E, Bengtsson-Palme J (2021) CAFE: A software suite for analysis of paired-sample transposon insertion sequencing data. Bioinformatics, advance article doi: 10.1093/bioinformatics/btaa1086
- Chao,M.C. et al. (2016) The design and analysis of transposon insertion sequencing experiments. Nature reviews Microbiology, 14, 119–128.
- van Opijnen,T. and Camilli,A. (2013) Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms. Nature reviews Microbiology, 11, 435–442.
- Goodman,A.L. et al. (2011) Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries. Nature Protocols, 6, 1969–1980.
- McCoy,K.M. et al. (2017) MAGenTA: a Galaxy implemented tool for complete Tn- Seq analysis and data visualization. Bioinformatics, 33, 2781– 2783.
- Zhao,L. et al. (2017) TnseqDiff: identification of conditionally essential genes in transposon sequencing studies. BMC Bioinformatics, 18.
- Zomer,A. et al. (2012) ESSENTIALS: Software for Rapid Analysis of High Throughput Transposon Insertion Sequencing Data. PLoS ONE, 7, e43012.
Today, we released a minor update to Metaxa2, bringing it to version 2.2.2. The new version includes some bug fixes related to the Metaxa2 Database Repository, as well as a new “–temp” option allowing the user to specify the location for the temporary files. No other changes have been made in this version.
The update can be found at the Metaxa2 software page.