miRBooking

Implementation of the miRBooking algorithm and metrics in C

• fast and memory efficient
• usable from Python, JavaScript and Vala via GObject introspection
• memory-mapped score tables, target and mirnas FASTA for low memory footprint in parallel execution
• binary with support for static linking for more portability
• stdin/stdout for piping from and into other tools

Usage

mirbooking --targets GCF_000001405.37_GRCh38.p11_rna.fna
           --mirnas mature.fa
           --seed-scores scores-7mer-3mismatch-ending
           [--accessibility-scores accessibility-scores[.gz]]
           [--supplementary-model none]
           [--supplementary-scores scores-3mer]
           [--input stdin]
           [--output stdout]
           [--output-format tsv]
           [--sparse-solver best-available]
           [--max-iterations 100]
           [--5prime-footprint 9]
           [--3prime-footprint 7]
           [--cutoff 100]
           [--relative-cutoff 0]

To obtain detailed usage and options, launch mirbooking --help.

The command line program expects a number of inputs:

• --targets, a FASTA containing RNA transcripts where the identifier is the accession (i.e. NM_002710.3)
• --mirnas, a FASTA containing mature miRNAs where the first token in the comment is the accession (i.e. MIMAT0004792)
• --seed-scores, a sparse score table of seed free energies which can be generated using generate-score-table program described below
• --accessibilitiy-scores contains entries with position-wise free energy contribution (or penalty) on the targets
• --supplementary-scores contains either 4mer or 3mer
• --input, a quantity file mapping target and mirna accessions to expressed quantity in picomolars

Tables for seed and supplementary scores are provided in the data folder. These were computed with RNAcofold binding energy from ViennaRNA package.

Note that Yan et al. (--supplementary-model=yan-et-al-2018) model requires a 3mer table whereas Zamore et al. (--supplementary-model=zamore-et-al-2012) require a 4mer table.

Tables for seed and supplementary bindings are automatically located (new in 2.3).

The --cutoff parameter can exploit a known upper bound on the complex concentration to adjust the granularity of the model. Only interaction that can ideally reach the specified picomolar concentration will be modeled.

The --relative-cutoff parameter is similar, but instead filter based on the ideal substrate bound fraction.

The output is a TSV with the following columns:

The detailed TSV output which expands the score structure in its constituents can be used with --output-format=tsv-detailed (new in 2.3). In this mode, the score column is replaced by kf, kr, kcleave, krelease, kcat, kother, kd and km.

The GFF3 output can be used with --output-format=gff3. The score will indicate the bound fraction of the position.

Wiggle output can also be produced with --output-format=wig. The score will be the position-wise bound fraction of substrate which properly account for overlapping microRNA.

Installation

You'll need Meson and Ninja as well as GLib development files installed on your system.

mkdir build && cd build
meson --buildtype=release
ninja
ninja install

To generate fast code, configure with meson -Doptimization=3.

You can perform a local installation using meson --prefix=$HOME/.local, but you'll need LD_LIBRARY_PATH set accordingly since the mirbooking program uses a shared library. Otherwise, a static linkage can be done by calling meson --default-library=static.

To generate introspection metadata, use meson -Dwith_introspection=true. To generate Vala bindings, use meson -Dwith_vapi=true.

FFTW can be optionally used to compute more accurate silencing by specifying meson -Dwith_fftw3=true. If you redistribute miRBooking source code, be careful not to enable this as a default because of the GPL license covering this dependency. If you have access to Intel MKL, you can alternatively use its FFTW3 implementation with -Dwith_mkl_fftw3=true.

OpenMP can be optionally used to parallelize the evaluation of partial derivatives and some supported solvers by specifying -Dwith_openmp=true.

MPI can be optionally used to distribute the computation across multiple machine on supported solvers (i.e. mkl-cluster) by specifying -Dwith_mpi=true.

cuSOLVER require CUDA toolkit whose API version is to be specified with -Dwith_cuda=<cuda_toolkit_api_version>.

MKL DSS and MKL Cluster can benefit from TBB instead of OpenMP, which can be enabled with -Dwith_mkl_tbb=true.

MKL DSS and MKL Cluster can be used with the 64 bit interface, allowing much larger systems to be solved with -Dwith_mkl_ilp64=true. However, this will break other solvers as it will load a 64 bit BLAS.

PARDISO cannot be used along with MKL DSS because they define common symbols.

By default, the best sparse solver available among the following will be used (new in 2.3):

1. MKL-DSS
2. PARDISO
3. UMFPACK
4. SuperLU (even if not available)

Numerical integration

In addition to determine the steady state, miRBooking can also perform numerical integration of the microtargetome using the programming API.

Other tools

In addition to the mirbooking binary, this package ship a number of utilities.

Te generate-score-table compute a hybridization energy table for a given seed mask. Either ViennaRNA or mcff is required to compute energies.

generate-score-table [--method=RNAcofold]
                     [--temperature=310.5]
                     [--mask=||||...]
                     [--hard-mask=||||...]
                      --output scores

The seed mask defines folding constraints on the target with | for a canonical match, x for a canonical mismatch and . for no constraint. It also determines the seed length. If a hard mask is provided, unsatisfying interactions are filtered out (new in 2.3).

It's also possible to ajust the folding temperature (new in 2.3).

The number of workers can be tuned by setting OMP_NUM_THREADS environment variable.

C API

The API is conform to the GLib style and enable a wide range of use. It is fairly easy to use and a typical experimentation session is:

1. create a broker via mirbooking_broker_new
2. create some sequence objects with mirbooking_target_new and mirbooking_mirna_new
3. setup quantities via mirbooking_broker_set_sequence_quantity
4. call mirbooking_broker_evaluate and mirbooking_broker_step repeatedly to perform a full hybridization or numerical integration
5. retrieve and inspect the microtargetome with mirbooking_broker_get_target_sites

For a more detailed usage and code example, the main program source in bin/mirbooking.c is very explicit as it perform a full session and fully output the target sites.