RAMTools - ROOT Alignment/Map Format Tools

RAMTools provides efficient tools for converting SAM files to ROOT's modern, columnar RNTuple format (RAM - ROOT Alignment/Map) and working with genomic alignment data.

Features

• High-performance SAM to RAM conversion
• Chromosome-based splitting for parallel processing
• Region-based querying capabilities

Requirements

• ROOT 6.26+
• C++17 compatible compiler
• CMake 3.16+

Quick Start

# 1. Build the tools
mkdir build && cd build
cmake ..
make -j$(nproc)

# 2. Convert a SAM file to the RAM format
./tools/samtoramntuple ../test/samexample.sam output.root

# 3. Query a specific region from the command line
./tools/ramntupleview output.root "chr1:15700-15800"

Command-Line Tools

The primary way to interact with RAMTools is through these command-line executables.

SAM to RAM Conversion

Convert a standard SAM file into the optimized RNTuple-based RAM format.

# Basic conversion
./tools/samtoramntuple input.sam output.root

# Split by chromosome for parallel processing
# (Creates output-chr1.root, output-chr2.root, etc.)
./tools/samtoramntuple input.sam output -split

Region Querying

Query a specific genomic region from a RAM file, similar to samtools view.

# Usage: ./tools/ramntupleview [input.root] "[chromosome]:[start]-[end]"
./tools/ramntupleview output.root "chr1:10150-10300"

Benchmark Results

Tested with the HG00154 sample from the 1000 Genomes Project (196M reads, 72.1 GB SAM). RAM is RNTuple-only. The same sample compresses to 11.4 GB.

Region Query Performance (LZMA)

Region	Time (s)	CPU (s)	Reads/sec	Total Reads
Small (100bp)	4.10	2.55	2.7	7
Gene (BRCA2)	1.20	1.15	37,308	42,961
10Mb	7.40	6.67	446,331	2,977,922
100Mb	6.46	6.36	448,688	2,852,438

Region Query Performance (LZ4)

Region	Time (s)	CPU (s)	Reads/sec	Total Reads
Small (100bp)	2.98	1.67	4.2	7
Gene (BRCA2)	1.01	0.948	45,321	42,961
10Mb	7.43	6.68	445,709	2,977,922
100Mb	6.47	6.29	453,736	2,852,438

Region Query Performance (ZLIB)

Region	Time (s)	CPU (s)	Reads/sec	Total Reads
Small (100bp)	2.85	1.73	4.0	7
Gene (BRCA2)	1.19	1.14	37,529	42,961
10Mb	7.40	6.62	449,599	2,977,922
100Mb	6.49	6.41	445,148	2,852,438

Key findings:

• Large-region throughput is about 445,000–454,000 reads/sec across LZMA, LZ4, and ZLIB
• LZ4 is the fastest of the three on the 100Mb query (453,736 reads/sec)
• Small 100bp queries are dominated by setup overhead (a few seconds for 7 reads)