The main page table

The main page table allows you to filter the data that you want displayed. There are several types of filters easy to apply, e.g. Growth temp., Cell length, Cell width, Growth pH, but more filters can be reached by choosing the field Columns (1). When you apply multiple filters, they are combined by the AND operator. You can also filter your data using operators such as equal to (=), greater than (>), less than (<), less than or equal to (<=), or greater than or equal to (>=). The example below shows how to display all methanogens that grow at temperatures greater than or equal to 20°C and less than or equal to 40°C and at pH greater than or equal to 6 and less than or equal to 8 (2). The filtered data can be easily downloaded by choosing CSV field (3). To clear filters chooses Reset Filters field (4) or reload the main page by clicking the PhyMet logo (5).

Adv. search

To filter the data that you want displayed, you can also use the Advanced search page. Compared to the main page table that uses operators such as = , > ,< , <=, >=, it allows you to filter your data using additional operators, i.e. NOT contain, Start with and End with. Select a searchable field from the menus (1), next the operator (2), than type the name, title or subject of interest (3) and click Search button. To do multiple filter searching, use additional operators (AND, OR) (4). You can also use the builder box to create your search (5).

The filtered data can be easily downloaded by choosing CSV field (6). To clear filters chooses Reset Filters field (7) or reload the main page by clicking the PhyMet logo (8).

Chart

We used Plotly to visualize some of methanogens feature contained in the database. You can create your own chart by selecting features of interests (1) and color palette (2). The dots in the chart represent species. You can check species name and values of their features by hovering the mouse cursor over the dots. To zoom in and out on the chart use the left or right mouse buttons. To reset magnified view select Reset axes (4). You can download the plot as a png file by clicking the camera button(3).

Sequence

Select protein or gene database (1) to search for genes or proteins by entering their name, part of their name or other identifier in the search box (2). You can specify it the result is to contain or not to contain the phrase entered by choosing contain or NOT contain filter (3). You can also use AND and OR operator (4) to do multiple filter searching.

Taxonomy

The taxonomy page allows you to quickly access methanogen records from a given Class, Order, Family, or Genus by simply selecting a given taxonomic group.

MethaGramPredictor

Using sequences of methyl coenzyme M reductase (mcrA) gene, we created a tool that helps evaluate optimal culturing conditions for unknown methanogens. MethaGramPredictor employs n-gram encoding of mcrA to create a set of informative features. Later, the random forests algorithm looks for an association between n-gram composition and the culturing requirements. In the 10-fold cross-validation, MethaGramPredictor had the following performance:

Homology

The homology search tools like the Blast algorithm find regions of local similarity between protein or nucleotide sequences. Our server allows to:

• search nucleotide databases of methanogens using a nucleotide sequence (Blastn)
• search protein databases of methanogens using a protein sequence (Blastp)
• search protein database of methanogens using translated nucleotide sequence (Blastx)
• search translated nucleotide database of methanogens using protein sequence (Tblastn)
• search translated nucleotide database of methanogens using translated nucleotide sequence (TBlastx)

• Expect threshold (E-value) tells us how many times an alignment with scores equivalent to or better than the observed one is expected to occur by chance. The lower the E-value, the more significant the alignment is, i.e. the greater the probability that the sequence represents a homologous sequence to the query sequence. The default value is assigned to 1.
• Scoring Matrix or substitution matrix represents the cost of replacing one amino acid by another. It is used to measure the similarity in sequence alignments. Contrary to the E-value, the higher the score, the better the alignment is.
• Gap opening penalty score represents the cost of creating a gap in the alignment.
• Gap extension penalty score represents the cost of making the already opened gap bigger.
• Match score represents the reward for two aligned nucleotides match.
• Mismatch score represents the penalty for replacing one nucleotide by another.
• Filtering low complexity regions allows to mask off repeated blocks of little diversity, e.g. GAAAAAAAAAAAAAAAAATAAAAAAAAAAAAAT that may influence searching for homologous genes or proteins.