Processes experimental RNA structure probing data (SHAPE-MaP, DMS-MaPseq) into per-nucleotide reactivity profiles with ShapeMapper2, then uses them as soft restraints on thermodynamic folding. Covers reagent and readout choice (SHAPE vs DMS, mutational-profiling vs RT-stop), the three control samples, per-transcript normalization, the Deigan vs Zarringhalam pseudo-energy models, in-cell versus in-vitro interpretation, and multi-conformation deconvolution. Use when converting probing reads to reactivities; deciding SHAPE versus DMS parameters; judging whether low reactivity means base-paired or protein-bound; or detecting whether an RNA populates more than one structure.
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Processes experimental RNA structure probing data (SHAPE-MaP, DMS-MaPseq) into per-nucleotide reactivity profiles with ShapeMapper2, then uses them as soft restraints on thermodynamic folding. Covers reagent and readout choice (SHAPE vs DMS, mutational-profiling vs RT-stop), the three control samples, per-transcript normalization, the Deigan vs Zarringhalam pseudo-energy models, in-cell versus in-vitro interpretation, and multi-conformation deconvolution. Use when converting probing reads to reactivities; deciding SHAPE versus DMS parameters; judging whether low reactivity means base-paired or protein-bound; or detecting whether an RNA populates more than one structure.
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
Python: pip show <package> then help(module.function) to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Structure Probing
"Process my SHAPE-MaP experiment to get RNA reactivity profiles" -> Convert per-nucleotide chemical-modification signal into reactivities, normalize them, then feed them as SOFT restraints into thermodynamic folding.
CLI: shapemapper (ShapeMapper2) for end-to-end SHAPE-MaP / DMS-MaP processing
CLI: RNAfold --shape (ViennaRNA) for reactivity-restrained folding
CLI: seismic (SEISMIC-RNA) for DMS-MaPseq and multi-conformation clustering
The governing principle: reactivity is flexibility, not pairing, and it is a constraint, not a structure
SHAPE reagents acylate the ribose 2'-OH at a rate set by local nucleotide FLEXIBILITY / conformational dynamics; DMS methylates the Watson-Crick face of A (N1) and C (N3). High reactivity means flexible/accessible, low means constrained. The routine over-interpretation is "low reactivity = base-paired": a nucleotide can be unreactive because it is base-paired OR because it is tertiary-contacted, protein-bound, ligand-occluded, or stacked. Reactivity probes CONSTRAINT, and it cannot by itself distinguish pairing from other protection. Before interpreting any low-reactivity region, rule out the TECHNICAL causes first: a long flat run can be low read depth / no-data or global undermodification, not structure -- check effective depth and confirm low-depth positions are carried as -999, not 0.
Two consequences govern everything below:
Reactivities are a RESTRAINT on folding, not a structure. They enter as pseudo-free-energy terms that bias the thermodynamic fold and raise accuracy substantially, but they do not yield a structure on their own and cannot disambiguate pairing from other protection.
A per-position profile is a POPULATION AVERAGE. If the RNA samples more than one structure (riboswitches, dynamic mRNAs), the averaged profile can match no real structure. Detecting and deconvolving multiple conformations requires per-read data (mutational profiling) and clustering (SEISMIC-RNA / DREEM), not a single profile.
Reagent and readout choices
SHAPE acts on the backbone 2'-OH, so it reports all four bases; DMS reads only A/C by default (G/U carry no Watson-Crick-face signal and must be masked to no-data before folding). Newer DMS-MaPseq protocols recover G(N1)/U(N3) signal for a four-base readout (via mutation-signature filtering plus optimized buffer, not the buffer alone) -- only treat DMS as four-base if the protocol and analysis explicitly enable it; otherwise mask G/U.
Goal
Reagent
Reads
Note
In-vitro, all-base, fast
1M7 (SHAPE)
A/C/G/U flexibility
default; m/b = 1.8/-0.6
In-cell SHAPE (membrane-permeable)
NAI, NAI-N3, 5NIA, 2A3
A/C/G/U
NAI-N3 -> icSHAPE click enrichment; 2A3 among the strongest in vivo
In-cell, cheap, A/C-resolved
DMS
A(N1)/C(N3)
works in vivo; mask G/U to no-data
Tertiary-contact flagging
1M6/NMIA vs 1M7 (differential SHAPE)
A/C/G/U
report the DIFFERENCE, not absolute
Fill G/U coverage
CMCT (G/U), kethoxal (G)
G,U / G
low throughput, rarely MaP-coupled
Mutational profiling (MaP) vs RT-stop is a fundamental analysis fork, not a detail: in MaP the reverse transcriptase reads THROUGH the adduct (Mn2+/TGIRT/Marathon-RT) and misincorporates, encoding each modification as a point mutation; in RT-stop the adduct truncates the cDNA and the read 5'-end is counted. They need different scoring, and a pipeline tuned for one is wrong for the other.
Axis
MaP (mutation)
RT-stop (truncation)
Adduct encoded as
misincorporation/deletion
RT drop-off (read 5'-end)
Reads/molecule informative
many
one
Single-molecule / correlated analysis
yes (per-read mutation strings)
no
Tools
ShapeMapper2, SEISMIC-RNA, rf-count -sm 3/4
rf-count -sm 1/2, icSHAPE, StructureFold
Methods
SHAPE-MaP, DMS-MaPseq
Mod-seq, Structure-seq, DMS-seq, icSHAPE
icSHAPE / Structure-seq / DMS-seq are RT-stop methods; do not push them through a MaP mutation-rate pipeline (ShapeMapper2/SEISMIC).
ShapeMapper2: the three samples and verified flags
The three sample roles are distinct: MODIFIED is the signal; UNTREATED subtracts background (SNPs, RT errors, intrinsic damage); DENATURED normalizes sequence-dependent reactivity bias (divide modified by denatured when no good no-data reference exists). The untreated control is mandatory; the denatured control improves normalization.
# ShapeMapper2 is Linux-only; on macOS use Docker/Singularity (see usage-guide).
shapemapper \
--target target_rna.fa \
--name my_rna \
--modified --R1 mod_R1.fastq.gz --R2 mod_R2.fastq.gz \
--untreated --R1 unmod_R1.fastq.gz --R2 unmod_R2.fastq.gz \
--out results/ \
--nproc 8 \
--min-depth 5000
Option
Effect (verified default)
--target / --name
reference FASTA / output basename
--modified / --untreated / --denatured
the three samples, each followed by --R1/--R2
--amplicon
primer-trimmed amplicon mode
--min-depth
minimum effective depth to report a nt (default 5000)
--min-qual-to-count
minimum basecall quality in a mutation (default 30, not 20)
--max-bg
max untreated mutation frequency (default 0.05)
--star-aligner
use STAR instead of Bowtie2 (recommended for long targets)
--nproc / --overwrite
threads / overwrite output
ShapeMapper2 writes a results/ tree. The reactivity table is <name>_<RNA>_profile.txt; the folder-ready files are SEPARATE: <name>_<RNA>.shape (2 columns: position, normalized reactivity; excluded = -999) and <name>_<RNA>.map (4 columns: position, normalized reactivity, stderr, base). There is no combined _map.shape file.
Key profile.txt columns: Nucleotide is the POSITION integer (1-based); Sequence is the base character; Reactivity_profile is raw; Norm_profile is after normalization. Fold with Norm_profile (or the .shape/.map file), never the raw Reactivity_profile -- the pseudo-energy parameters assume normalized input.
Normalization: per-transcript, and why raw reactivities are not comparable
Raw reactivity (background-subtracted modified mutation rate) sits on an arbitrary, experiment-specific scale set by reagent dose, RT efficiency, and depth. The standard 2-8% / box-plot normalization excludes outliers (top ~2% as a whisker cap), then scales by the mean of the next most-reactive ~8-10% of nucleotides, so normalized values mostly fall ~0-2 with ~1.0 = average reactivity. This scale factor is PER TRANSCRIPT: raw reactivities from different transcripts or experiments are NOT comparable, so never pool or compare raw reactivities across them. To compare two conditions (e.g. +/- ligand), use delta-SHAPE at matched positions with the per-nt standard errors, not raw subtraction. Low-depth nucleotides must become no-data (-999), not zero.
Reactivity-restrained folding and the pseudo-energy model
The Deigan model adds a soft pseudo-energy to every nucleotide in a stacked pair: deltaG = m * ln(1 + reactivity) + b. It is a restraint, not a hard constraint -- a nucleotide can still pair against the data if the global fold demands it.
Situation
Model / ViennaRNA flag
Parameters
Standard SHAPE (1M7/NAI)
Deigan --shapeMethod="Dm1.8b-0.6"
m=1.8, b=-0.6 (Hajdin 2013)
Noisy data / probabilistic target
Zarringhalam --shapeMethod="Z"
target pairing probability
Penalize unpaired only
Washietl --shapeMethod="W"
perturbation vector
DMS-MaPseq
Deigan-style, A/C only, G/U set to -999
no DMS-specific standard; commonly reuse 1.8/-0.6, or tune
The m=1.8, b=-0.6 pair is the Hajdin et al. 2013 standard and the ViennaRNA "Deigan" DEFAULT -- it is NOT Deigan et al. 2009's own values (m=2.6, b=-0.8); cite it correctly. For DMS, apply the restraint ONLY to A/C and set G/U to -999, or the model invents constraints at bases that carry no signal. The folded energy a tool reports after a SHAPE/DMS restraint INCLUDES the pseudo-energy bonus, so it is not comparable to an unrestrained MFE -- judge the result by structure agreement, not by a more-negative energy.
# Fold directly from the ShapeMapper2 .shape file (already normalized)
RNAfold --shape=results/my_rna_my_rna.shape --shapeMethod="Dm1.8b-0.6" --noPS < target_rna.fa
In-cell vs in-vitro: the occupancy trap
In-vitro (refolded, deproteinized) RNA reports pure thermodynamics; in-cell reports the RNA as it exists, with bound proteins, ligands, and chaperone-remodeled states all altering reactivity. An in-cell PROTECTED nucleotide may be protein-bound or ligand-occluded, not base-paired -- the single biggest in-cell misinterpretation. Cells also actively unfold mRNA: genome-wide in-vivo DMS showed mRNAs are MORE unfolded in vivo than in vitro (Rouskin 2014). The in-cell-minus-in-vitro difference is itself the signal for protein/ligand footprints (Spitale 2015).
Want
Condition
Reagent
Caveat
De-novo thermodynamic structure
in-vitro refolded
1M7 SHAPE / DMS
MFE-like, no proteins
Functional in-cell state
in-cell
NAI/2A3/5NIA, DMS
protected != paired (occupancy)
Protein/ligand footprint
in-cell vs in-vitro delta
matched reagent
needs both, matched depth
Multiple conformations: cluster, do not average
If a profile looks inconsistent with any single structure, the RNA may populate more than one. DREEM (Tomezsko 2020) and its maintained successor SEISMIC-RNA cluster MaP reads by co-occurring mutations (expectation-maximization) to deconvolve coexisting conformers; RING-MaP (Homan 2014) and PAIR-MaP (Mustoe 2019) use correlated mutations between positions to detect through-space communication and direct base pairs. These need per-read mutation data, which only MaP provides.
# DMS-MaPseq processing and multi-conformation clustering with SEISMIC-RNA# Subcommand names vary by version (released: align/relate/mask/cluster; recent dev renames# relate->idmut, mask->filter). Run `seismic --help` to confirm before scripting.
seismic align target.fa reads_R1.fq.gz reads_R2.fq.gz --out seismic_out
seismic relate seismic_out target.fa --out seismic_out
seismic mask seismic_out --out seismic_out
seismic cluster seismic_out --max-clusters 3 --out seismic_out
For RNA Framework (rf-count -> rf-norm), the reference is -f and the BAM/SAM files are positional; choose the MaP-vs-RT-stop scoring in rf-norm with -sm (1 Ding RT-stop, 2 Rouskin RT-stop, 3 Siegfried MaP, 4 Zubradt MaP) and the normalization with -nm (1 = 2-8% default, 3 = box-plot); restrict reactive bases for DMS with -rb AC.
reference is -f; BAMs are positional; there is no -r/-rc
Two conditions disagree but raw reactivities were compared
raw values are per-transcript, non-comparable
compare normalized profiles (delta-SHAPE) with standard errors
Profile fits no single structure
RNA populates multiple conformations
cluster MaP reads with SEISMIC-RNA / DREEM
In-cell protected region called "paired"
protection may be protein/ligand occupancy
compare in-cell vs in-vitro; do not equate protection with pairing
A long unreactive stretch read as a stable hairpin
could be low depth/no-data or undermodification, not pairing
check effective depth (>=5000) and that low-depth nt are -999 before interpreting
Related Skills
secondary-structure-prediction - The folding engine the reactivities restrain
ncrna-search - Identify the RNA family and a CM consensus structure to probe against
covariation-analysis - Independent (evolutionary) evidence for the pairs probing suggests
epitranscriptomics/m6a-peak-calling - RNA modifications that confound DMS/SHAPE reactivity
clip-seq/binding-site-annotation - In-cell protection as an RBP footprint
read-qc/quality-reports - QC of the underlying sequencing reads
References
Merino EJ, Wilkinson KA, Coughlan JL, Weeks KM. 2005. RNA structure analysis at single nucleotide resolution by selective 2'-hydroxyl acylation and primer extension (SHAPE). J Am Chem Soc 127(12):4223-4231. doi:10.1021/ja043822v
Mortimer SA, Weeks KM. 2007. A fast-acting reagent for accurate analysis of RNA secondary and tertiary structure by SHAPE chemistry. J Am Chem Soc 129(14):4144-4145. doi:10.1021/ja0704028
Deigan KE, Li TW, Mathews DH, Weeks KM. 2009. Accurate SHAPE-directed RNA structure determination. Proc Natl Acad Sci USA 106(1):97-102. doi:10.1073/pnas.0806929106
Zarringhalam K, Meyer MM, Dotu I, Chuang JH, Clote P. 2012. Integrating chemical footprinting data into RNA secondary structure prediction. PLoS ONE 7(10):e45160. doi:10.1371/journal.pone.0045160
Hajdin CE, Bellaousov S, Huggins W, Leonard CW, Mathews DH, Weeks KM. 2013. Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots. Proc Natl Acad Sci USA 110(14):5498-5503. doi:10.1073/pnas.1219988110
Cordero P, Kladwang W, VanLang CC, Das R. 2012. Quantitative dimethyl sulfate mapping for automated RNA secondary structure inference. Biochemistry 51(36):7037-7039. doi:10.1021/bi3008802
Rouskin S, Zubradt M, Washietl S, Kellis M, Weissman JS. 2014. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505(7485):701-705. doi:10.1038/nature12894
Homan PJ, Favorov OV, Lavender CA, Kursun O, Ge X, Busan S, Dokholyan NV, Weeks KM. 2014. Single-molecule correlated chemical probing of RNA. Proc Natl Acad Sci USA 111(38):13858-13863. doi:10.1073/pnas.1407306111
Siegfried NA, Busan S, Rice GM, Nelson JAE, Weeks KM. 2014. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat Methods 11(9):959-965. doi:10.1038/nmeth.3029
Smola MJ, Rice GM, Busan S, Siegfried NA, Weeks KM. 2015. Selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP) for direct, versatile and accurate RNA structure analysis. Nat Protoc 10(11):1643-1669. doi:10.1038/nprot.2015.103
Spitale RC, Flynn RA, Zhang QC, Crisalli P, Lee B, Jung JW, Kuchelmeister HY, Batista PJ, Torre EA, Kool ET, Chang HY. 2015. Structural imprints in vivo decode RNA regulatory mechanisms. Nature 519(7544):486-490. doi:10.1038/nature14263
Zubradt M, Gupta P, Persad S, Lambowitz AM, Weissman JS, Rouskin S. 2017. DMS-MaPseq for genome-wide or targeted RNA structure probing in vivo. Nat Methods 14(1):75-82. doi:10.1038/nmeth.4057
Busan S, Weeks KM. 2018. Accurate detection of chemical modifications in RNA by mutational profiling (MaP) with ShapeMapper 2. RNA 24(2):143-148. doi:10.1261/rna.061945.117
Incarnato D, Morandi E, Simon LM, Oliviero S. 2018. RNA Framework: an all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications. Nucleic Acids Res 46(16):e97. doi:10.1093/nar/gky486
Mustoe AM, Lama NN, Irving PS, Olson SW, Weeks KM. 2019. RNA base-pairing complexity in living cells visualized by correlated chemical probing (PAIR-MaP). Proc Natl Acad Sci USA 116(49):24574-24582. doi:10.1073/pnas.1905491116
Tomezsko PJ, Corbin VDA, Gupta P, Swaminathan H, Glasgow M, Persad S, Edwards MD, Rouskin S. 2020. Determination of RNA structural diversity and its role in HIV-1 RNA splicing (DREEM). Nature 582(7812):438-442. doi:10.1038/s41586-020-2253-5
