@datagrok/bio

Bio

Bio is a bioinformatics support package for the Datagrok platform with an extensive toolset supporting SAR analisys for small molecules and antibodies.

Notations

@datagrok/bio can ingest data in multiple file formats (such as fasta o csv) and multiple notations for natural and modified residues, aligned and non-aligned forms, nucleotide and amino acid sequences. The sequences are automatically detected and classified, while preserving their initial notation. Datagrok allows you to convert sequences between different notations as well.

See:

• detectMacromolecule()
• class NotationConverter

Atomic-Level structures from sequences

For linear sequences, the linear form (see the illustration below) of molecules is reproduced. This is useful for better visual inspection of sequence and duplex comparison. Structure at atomic level could be saved in available notations.

You can easily run this feature for any sequence data using the Bio package and accessing it from the top menu.

See:

• getMolfilesFromSeq()

MSA

For multiple-sequence alignment, Datagrok uses the “kalign” that relies on Wu-Manber string-matching algorithm [Lassmann, Timo. Kalign 3: multiple sequence alignment of large data sets. Bioinformatics (2019).pdf]. “kalign“ is suited for sequences containing only natural monomers. Sequences of a particular column can be analyzed using MSA algorithm available at the top menu. Aligned sequences can be inspected for base composition at the position of MSA result.

See:

• runKalign()

TODO: MSA with PepSeA

Splitting to monomers

Splitting to monomers allows splitting aligned sequences in separate monomers.

See:

• splitAlignedSequences()

Web Logo

Web Logo is used to visualize a graphical representation of multiple sequence alignment (amino acids or nucleotides). Each logo consists of stacks of symbols, one stack for each position in the sequence. The overall height of the stack indicates the sequence conservation at that position, while the height of symbols within the stack indicates the relative frequency of each amino at that position.

In general, a sequence logo provides a richer and more precise description of, for example, a binding site, than would a consensus sequence.

You must specify the tag semType with value AminoacidsMultipleAlignment or NucleotidesMultipleAlignment for the data column with multiple alignment sequences, it is mandatory to select the palette for monomers' colors.

You can customize the look of the viewer with properties. Properties startPosition and endPosition) allow to display multiple alignment partially. If property startPosition (endPosition) is not specified, then the Logo will be plotted from the first (till the last) position of sequences.

General

Right click	Context menu

Properties

Property name	Default	Description
positionWidth	16	Width of one position stack [px]
minHeight	50	Minimum height of Logo [px]
maxHeight	100	Maximum height of Logo [px]
considerNullSequence	false	Should logo consider null seqences of data
sequenceColumnName	null	source of multiple alignment sequences (column name)
startPositionName	null	name of the first position to display Logo partially
endPositionName	null	name of the last position to display Logo partially
fixWidth	false	Plot takes full width required for sequence length
verticalAlignment	'middle'	choices: ['top', 'middle', 'bottom']
horizontalAlignment	'center'	choices: ['left', 'center', 'right']
fitArea	true	Should control to be scaled to fit available area for viewer
shrinkEmptyTail	true	Shrink sequences' tails empty in filtered sequences
skipEmptyPositions	false	Skip positions containing only gap symbols in all sequences
positionMarginState	'auto'	choices: ['auto', 'enable', 'off'] Margin between positions. auto - enables margins for sequences of multichar monomers
positionMargin	0 or 4	4 - for sequences of multichar monomers, 0 - single char
positionHeight	'100%'	choices: ['100%', 'Entropy'] The way to calculate overall monomers stack height at position

See also:

• WebLogo
• Viewers
• Table view

Sequence space

Datagrok allows visualizing multidimensional sequence space using a dimensionality reduction approach. Several distance-based dimensionality reduction algorithms are available, such as UMAP or t-SNE. The sequences are projected to 2D space closer if they correspond to similar structures, and farther otherwise. The tool for analyzing molecule collections is called 'Sequence space' and exists in the Bio package.

To launch the analysis from the top menu, select Bio | Sequence space.

See:

• sequenceSpace()

Sequence activity cliffs

Activity cliffs tool finds pairs of sequences where small changes in the sequence yield significant changes in activity or any other numerical property. open the tool from a top menu by selecting. Similarity cutoff and similarity metric are configurable. As in Sequence space, you can select from different dimensionality reduction algorithms.

To launch the analysis from the top menu, select Bio | Sequence Activity Cliffs.

See:

• getActivityCliffs()