We use GitHub for version control on our software. All members of the lab have their own pages, and there are also common repos for things we work on together. Here are links to our repositories where you can find what we worked on before, and what we’re working on now!
We also have some tips on making software releases using GitHub and branches.
We are interested in phages — viruses that infect bacteria. For years the Edwards’ lab has been looking at new, undiscovered phages.
Recently, we identified the crAssphage, a new type of virus that has never been seen before. By looking at the sequences in metagenomes we were able to identify a set of contigs that were common among many different metagenomes. When we assembled them, they looked like a phage. We could compare them to other known phages in our database of sequences.
Working with folks in the biology department we proved that this is a circular virus by using PCR. However, we have so far been unable to culture the virus in vivo. We’re working on it, and hopefully others are too, but until that point we don’t have an image of the virus or an idea of what it does.
The US-Brazilian Consortium for Marine Sciences is funded by the Department of Education through its Fund for the Improvement of Postsecondary Education (FIPSE), and the Fundacao Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) from the Brazilian Ministry of Education. We’ve assembled a team of marine sciences researchers from San Diego State University and Scripps Institution of Oceanography, together with a team from the Federal University of Rio de Janeiro (UFRJ), the Universidade Federal de Pernambuco, and Universidade Federal da Paraíba, together with FIOCRUZ and the Rio de Janeiro Botanical Gardens. Together, we will develop a completely new marine sciences course to be held in Brazil in 2011 and 2012, and exchange students between San Diego, Rio de Janeiro, Pernambuco, and Paraíba.
The viral dark matter is all the sequences that we find in metagenomes that we don’t know what to do with. In a project funded by the National Science Foundation, together with Dr. Forest Rohwer and Dr. Anca Segall in the SDSU Biology Department, and Dr. Alex Burgin, we will tackle some of this dark matter. We’re going to combine metagenomics, metaproteomics, metabolomics, and structural biology to unearth the functions of sets of genes that we have no idea what they do.
Finding prophages in microbial genomes remains a problem with no definitive answer. The majority of existing tools rely on detecting genomic regions enriched in proteins with known phage homologs, which hinders the de novo discovery of phage regions. In this study, a weighted phage detection algorithm, Phage_detector was developed based on seven distinctive characteristics of prophages i.e. protein length, transcription strand directionality, customized AT and GC skew, the abundance of unique phage words, phage insertion points and the similarity of phage proteins. The first five characteristics are capable of identifying prophages without any sequence similarity with known phage genes. Phage_detector locates prophages by ranking genomic regions enriched in distinctive phage traits, which leads to the successful prediction of 92% of prophages (including 33 previously unidentified prophages) in 95 complete bacterial genomes with 8% false negative and 18% false positive.
There are two distinct phage lifestyles: lytic and lysogenic. The lysogenic lifestyle has many implications for phage therapy, genomics, and microbiology, however it is often very difficult to determine whether a newly sequenced phage isolate grows lytically or lysogenically just from the genome. Using the ~200 known phage genomes, a supervised random forest classifier was built to determine which proteins of phage are important for determining lytic and lysogenic traits. A similarity vector is created for each phage by comparing each protein from a random sampling of all known phage proteins to each phage genome. Each value in the similarity vector represents the protein with the highest similarity score for that phage genome. This vector is used to train a random forest to classify phage according to their lifestyle. To test the classifier each phage is removed from the data set one at a time and treated as a single unknown. The classifier was able to successfully group 188 of the 196 phages for whom the lifestyle is known, giving my algorithm an estimated 4% error rate. The classifier also identifies the most important genes for determining lifestyle; in addition to integrases, expected to be important, the composition of the phage (capsid and tail) also determines the lifestyle. A large number of hypothetical proteins are also involved in determining whether a phage is lytic or lysogenic.
Metagenome analysis spans a large range of different methods and tools in the bioinformatics community. These tools provide scientists with biological information present in a sequenced environmental sample, more specifically the genetic functions encoded in the DNA of the sampled metagenome. Most often those tools have been developed to compare a specific metagenome file against databases that are filled with sequences and annotation data.
This project is directed to performing a comparative analysis between multiple metagenomic FASTA files. By importing n-length pieces of the sequences from one file into a hash table structure, comparing other metagenome sequences from other files will be done quickly and precisely. Finding similar sequences and structures between numerous metagenomes can give insight into what biological functions are shared between related and unrelated organisms.
A project that started with the question, “how many microbial genes are there in the world?” has grown to potentially lead to answers to this and broader questions about the microbial universe. First, known taxa (E. coli) were organized into matrices, with strains as rows, and proteins as columns. Hamming distances define a metric for organizing strains into phylogenetic trees. The phylogenetic distance is the importance of the split between the strains, or the alpha score, as refered to in d-splits literature. This approach became our main focus when we attempted the same heuristic with viral data, with surprisingly strong results. At present, we are taking “pie slices” of the phage proteonomic tree, and seeing to what extent we can recreate that observed internal structure, as a “proof of concept” for viral applicability. Reading and work on splitstrees, d-splits, and consecutive ones property, will drive the next developments. In addition, this coming week, on August 18th, our group will be attending a lecture on whole genome taxonomy, which should help drive further progress on our project.
Phages are the most abundant biological entities on the planet and have had tremendous impact on biological sciences; however, phage genomes lag behind bacterial and eukaryotic genomes in the quality of annotation. For this purpose, the PhAnToMe project was launched to establish a phage annotation database, a rapid annotation pipeline for phage genomes (PhiRAST or phage Rapid Annotation Using Subsystem Technology), and a graphic programming interface for biologists (using the BioBIKE interface).
The PhAnToMe project involves multiple research centers in the United States and includes several stages. The SDSU center is in charge of developing phage genomic subsystems, phage protein families (FIGFams), and subsequently the first release of PhiRAST. As a member of this team, I am in charge of building or coordinating subsystems, and of establishing links with the phage research community. In addition, once the PhiRAST is developed, I will also be in charge of coordinating training workshops and developing testable hypotheses based on the PhAnToMe annotations and subsystems.