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by com.seqbench

Predict translation initiation rate (OSTIR)

rbs_predict
Read-onlyIdempotent

Predict the translation initiation rate at each start codon in a bacterial mRNA using OSTIR, the open-source continuation of the Salis lab RBS Calculator, with ViennaRNA free energies. Returns the predicted rate plus the full thermodynamic breakdown (16S rRNA:mRNA hybridisation, mRNA unfolding, spacing, standby site, start-codon binding) for every start codon found. Rates are on an arbitrary scale — compare them as ratios, not as absolute expression levels. Runs ViennaRNA on a shared service and is therefore rate limited (see 429/503). PREDICTED, NOT MEASURED (Spearman ρ = 0.39 on two 5' UTR datasets it was not fitted to). Spearman ρ = 0.39 against measured expression on two 5' UTR datasets it was not fitted to (Gilliot & Gorochowski, Nucleic Acids Res 2024;52(13):e58). The widely quoted 53% within 2-fold / 91% within 10-fold are calibration residuals on the fitting set, not held-out validation. Valid for: translation INITIATION only, in E. coli-like Gram-negative hosts (the model is parameterised on the E. coli anti-Shine-Dalgarno sequence). Rankings within one construct context; the absolute value has no units and no meaning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoOptional 1-based position; only consider start codons beginning at or before it.
startNoOptional 1-based position; only consider start codons beginning at or after it.
sequenceYesmRNA sequence, raw or FASTA — the 5' UTR plus at least the start of the CDS. DNA (T) and RNA (U) are both accepted and scored identically. A, C, G, T/U only. Max 3,000 nt.
antiShineDalgarnoNoOptional anti-Shine-Dalgarno sequence (the 16S rRNA 3' end) to model a non-E. coli host. Omit to use OSTIR's own E. coli default.

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes far beyond annotations (readOnlyHint, idempotentHint) by disclosing rate limiting (429/503 errors), shared service usage, return content (thermodynamic breakdown), the predictive accuracy (Spearman ρ=0.39), and the pitfalls of commonly cited statistics (calibration residuals). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is comprehensive but verbose (~150 words). It is front-loaded with the main action and each sentence adds value, but it repeats the Spearman correlation and could be trimmed without losing meaning. Adequate but not optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a prediction tool with 4 parameters and no output schema, the description thoroughly explains what the tool returns (full thermodynamic breakdown), its limitations (arbitrary scale, validation stats), rate limits, and acceptable inputs. No gaps in context given the complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters have schema descriptions (100% coverage). The tool description adds meaning beyond schema by specifying sequence requirements (5' UTR + CDS, DNA/RNA accepted, max 3000 nt) and explaining how antiShineDalgarno models non-E. coli hosts. This extra context raises it above the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it predicts translation initiation rate using OSTIR for bacterial mRNA, specifying the start codons, return value (rate and thermodynamic breakdown), and distinguishes it from sibling tools like rbs_design by emphasizing prediction over design.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context on when to use (translation initiation only, E. coli-like hosts, rankings within a construct) and limitations (arbitrary scale, predicted vs measured, validation caveats). However, it lacks explicit differentiation from alternatives like rbs_design or gene_expression, and does not state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

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