Secondary Structure Detection

CRESSENT provides specialized tools for detecting and analyzing secondary structures in CRESS DNA viruses, focusing on two critical elements: stem-loop structures and iterons. These features are essential for understanding viral replication mechanisms and genomic organization.

_images/fig_module_2ry_str.png

Overview

The secondary structure module includes:

  • Stem-loop detection using RNA folding algorithms and motif conservation

  • Iteron identification with CRUISE integration for replication origins

  • Structural validation through energy minimization and pattern recognition

  • Annotation output in standard GFF3 format

Stem-Loop Detection

Purpose

Identify hairpin structures with conserved motifs, particularly important for viral replication and packaging signals.

Basic Usage

cressent sl_finder -i sequences.fasta \
                  --gff_in annotations.gff \
                  --out_gff stemloops.gff \
                  --output sl_analysis/

Algorithm Overview

The stem-loop finder uses a multi-step approach:

  1. Motif searching for conserved sequences (e.g., nonanucleotides)

  2. RNA folding using ViennaRNA package

  3. Structure parsing to identify stem-loop patterns

  4. Validation based on stem/loop length criteria

  5. Scoring using structural and sequence features

Parameters

Parameter

Description

Default

--motif

Conserved motif pattern

nantantan

--family

CRESS viral family

None

--idealstemlen

Ideal stem length

11

--ideallooplen

Ideal loop length

11

--frame

Bases around motif for folding

15

--csv_out

Output CSV filename

None

Viral Family Support

Pre-defined motifs for major CRESS virus families:

Family

Motif Pattern

Geminiviridae

TRAKATTRC

Circoviridae

TAGTATTAC

Cycloviridae

TAATATTAC

Genomoviridae

TAWWDHWAN

Smacoviridae

NAKWRTTAC

General

TAGTATTAC

Example Usage by Family

# Geminivirus analysis
cressent sl_finder -i gemini_genomes.fasta \
                  --gff_in annotations.gff \
                  --family geminiviridae \
                  --out_gff gemini_stemloops.gff \
                  --output gemini_analysis/

# Custom motif analysis
cressent sl_finder -i sequences.fasta \
                  --gff_in annotations.gff \
                  --motif "TAGTATTAC" \
                  --idealstemlen 12 \
                  --ideallooplen 8 \
                  --out_gff custom_stemloops.gff \
                  --output custom_analysis/

Output Files

  • GFF annotations (stemloops.gff): Stem-loop and nonanucleotide features

  • CSV results (results.csv): Detailed structural information

  • Log file (sl_finder.log): Processing details and statistics

GFF Output Format

##gff-version 3
sequence_01    sl_finder    stem_loop         150    201    .    +    .    Name=stem-loop
sequence_01    sl_finder    nonanucleotide    165    173    .    +    .    Name=nona
sequence_02    sl_finder    stem_loop         89     145    .    -    .    Name=stem-loop
sequence_02    sl_finder    nonanucleotide    110    118    .    -    .    Name=nona

CSV Output Format

seqID

matched

motif_start

stem_start

stem_end

score

folded_structure

sequence_01

TAGTATTAC

165

150

201

8.5

(((((…….)))))…..

sequence_02

TAGTATTAC

110

89

145

6.2

(((((……..)))))…..

Iteron Detection

Purpose

Identify iterons (direct repeats) that serve as replication origins in CRESS DNA viruses, using the CRUISE algorithm.

Basic Usage

cressent run_cruise --input_fasta sequences.fasta \
                   --inputGFF annotations.gff \
                   --output cruise_analysis/ \
                   --outputGFF iterons.gff

CRUISE Algorithm

CRUISE (CRUcivirus Iteron SEarch) implements:

  1. Substring enumeration within specified length ranges

  2. Distance analysis between repeat occurrences

  3. Stem-loop context validation

  4. Scoring systems based on repeat quality and distribution

  5. Known iteron database comparison

Parameters

Parameter

Description

Default

--minLength

Minimum iteron length

5

--maxLength

Maximum iteron length

12

--range

Search range around nonanucleotide

65

--rank

Use ranking system

True

--numberTopIterons

Number of top iterons to report

5

--maxScore

Maximum score for non-ranked mode

40

--wiggle

Length-distance tolerance

5

--goodLength

Optimal iteron length

11

--maxDist

Maximum distance between iterons

20

--bestDist

Optimal distance between iterons

10

Known Iterons Database

CRUISE includes a database of characterized iterons:

# Examples of known iterons
TTGTCCAC  # RDHV
AGTGGGA   # Various circoviruses
GCCACCC   # Begomovirus
GGGGA     # Mastrevirus
TCTGA     # Curtovirus

Advanced Parameters

cressent run_cruise --input_fasta sequences.fasta \
                   --inputGFF annotations.gff \
                   --output detailed_analysis/ \
                   --minLength 8 \
                   --maxLength 15 \
                   --range 100 \
                   --numberTopIterons 10 \
                   --maxDist 25 \
                   --bestDist 12 \
                   --scoreRange 30

Output Files

  • Annotated GFF (iterons.gff): Original GFF with iteron annotations

  • Processing log (cruise.log): Detailed analysis log

  • Statistics summary: Iteron discovery rates and patterns

Iteron Annotation Types

The CRUISE module annotates several types of features:

Feature Type

Description

iteron

Standard direct repeats

stem_loop_repeats

Iterons associated with stem-loops

tagIteron

Known/characterized iterons

known_iteron

Database-matched sequences

Combined Secondary Structure Analysis

Integrated Workflow

# Step 1: Identify stem-loops
cressent sl_finder -i cress_genomes.fasta \
                  --gff_in base_annotations.gff \
                  --family circoviridae \
                  --out_gff stemloops.gff \
                  --csv_out stemloop_details.csv \
                  --output structure_analysis/

# Step 2: Find iterons in context
cressent run_cruise --input_fasta cress_genomes.fasta \
                   --inputGFF structure_analysis/stemloops.gff \
                   --output structure_analysis/ \
                   --outputGFF complete_structures.gff \
                   --minLength 6 \
                   --maxLength 14 \
                   --numberTopIterons 8

Quality Control Pipeline

# Conservative stem-loop detection
cressent sl_finder -i sequences.fasta \
                  --gff_in annotations.gff \
                  --idealstemlen 10 \
                  --ideallooplen 9 \
                  --frame 20 \
                  --out_gff conservative_stemloops.gff \
                  --output qc_analysis/

# Comprehensive iteron search
cressent run_cruise --input_fasta sequences.fasta \
                   --inputGFF qc_analysis/conservative_stemloops.gff \
                   --output qc_analysis/ \
                   --minLength 4 \
                   --maxLength 16 \
                   --range 80 \
                   --numberTopIterons 15 \
                   --maxDist 30

Validation and Quality Assessment

Structural Validation

  1. Energy scoring using ViennaRNA folding energies

  2. Length criteria for biologically relevant structures

  3. Motif conservation across related sequences

  4. Distance constraints for iteron spacing

Quality Metrics

# High-confidence stem-loops
- Stem length: 8-15 bp
- Loop length: 6-20 bp
- Folding score: < 10
- Motif match: exact or 1 mismatch

# Validated iterons
- Length: 6-12 bp
- Occurrences: ≥ 2
- Distance: 8-15 bp
- Context: within 65 bp of nonanucleotide

Biological Interpretation

Stem-Loop Functions

  • Replication origins (often contain nonanucleotides)

  • Packaging signals for viral DNA

  • Regulatory elements for gene expression

  • Structural domains in viral genomes

Iteron Significance

  • Replication initiation sites

  • Rep protein binding domains

  • Copy number control elements

  • Evolutionary markers for virus classification

Best Practices

Input Preparation

  1. Quality sequences with minimal gaps or ambiguities

  2. Proper annotations in GFF3 format

  3. Family-specific parameters when known

  4. Consistent naming for sequence identifiers

Parameter Optimization

  1. Family-appropriate motifs for stem-loop detection

  2. Realistic length ranges for iteron searches

  3. Contextual distances based on genome organization

  4. Scoring thresholds appropriate for data quality

Validation Strategies

  1. Cross-reference with known structures

  2. Phylogenetic consistency across related sequences

  3. Experimental validation when possible

  4. Literature comparison for characterized viruses

Troubleshooting

Common Issues

No stem-loops detected:

  • Verify motif pattern accuracy

  • Check sequence quality and format

  • Adjust folding parameters

  • Consider alternative viral families

Excessive iteron predictions:

  • Increase scoring stringency

  • Reduce maximum distance parameters

  • Use ranking mode for top candidates

  • Filter by stem-loop context

ViennaRNA errors:

  • Check sequence format (DNA/RNA)

  • Verify installation completeness

  • Monitor memory usage for large sequences

  • Consider sequence length limitations

Performance Optimization

Large genomes:

  • Process in smaller segments

  • Increase memory allocation

  • Use parallel processing where available

  • Consider preliminary filtering

Parameter tuning:

  • Start with default values

  • Adjust based on known biology

  • Validate with characterized examples

  • Document parameter choices

Error Diagnostics

# Detailed logging output
INFO - Processing record: sequence_01
INFO - Found stem-loop at 150-201 in sequence_01
WARNING - No iterons found in sequence_02
ERROR - ViennaRNA folding failed: sequence too short