diff --git a/README.md b/README.md
index e1c5a89..4d65113 100644
--- a/README.md
+++ b/README.md
@@ -17,3 +17,62 @@ conda install -c bioconda lemur
 ### Obtaining the database
 The current database (RefSeq v221 bacterial and archaeal genes, and RefSeq v222 fungal genes) is available at [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10802546.svg)](https://doi.org/10.5281/zenodo.10802546)
 
+## Usage
+
+### Basic usage
+
+For minimal example you will need to specify the following parameters: the input FASTQ file containing the reads (`-i/--input` flag), a directory to store the Lemur output (`-o/--output` flag), path to the database directory (`-d/--db-prefix` flag), path to the taxonomy file in the TSV format (`--tax-path` flag), and desired taxonomic aggregation rank (`-r/--rank` flag).
+
+```
+lemur -i examples/example-data/example.fastq \
+      -o example-output \
+      -d examples/example-db \
+      --tax-path examples/example-db/taxnomy.tsv \
+      -r species
+```
+
+The output in the `example-output` folder will consist of raw `relative_abundance.tsv` file with taxonomic IDs, lineage information, and inferred relative abundance (`F` column). There will also be a `relative_abundance-[rank].tsv` where the rank is specified by the `-r/--rank` flag (e.g. in the above example it will be `species`). The `*P_rgs_df*` files capture individual inferred probabilities of a given read comign from a particular taxon. 
+
+### Parameter descriptions
+
+Main arguments:
+```
+  -i INPUT, --input INPUT
+                        Input FASTQ file for the analysis
+  -o OUTPUT, --output OUTPUT
+                        Folder where the Lemur output will be stored
+  -d DB_PREFIX, --db-prefix DB_PREFIX
+                        Path to the folder with marker gene DB for each marker gene
+  --tax-path TAX_PATH   Path to the taxonomy.tsv file 
+  -t NUM_THREADS, --num-threads NUM_THREADS
+                        Number of threads you want to use
+  --aln-score {AS,edit,markov}, --aln-score {AS,edit,markov}
+                        AS: Use SAM AS tag for score, edit: Use edit-type distribution for score, markov: Score CIGAR as Markov chain
+  -r RANK, --rank RANK  Taxonomic rank used for final aggregation
+```
+
+minimap2 arguments:
+```
+  --mm2-N MM2_N         minimap max number of secondary alignments per read [50]
+  --mm2-K MM2_K         minibatch size for minimap2 mapping [500M]
+  --mm2-type {map-ont,map-hifi,map-pb,sr}
+                        ONT: map-ont [map-ont], PacBio (hifi): map-hifi, PacBio (CLR): map-pb, short-read: sr
+```
+
+Miscellaneous arguments:
+```
+  --keep-alignments     Keep SAM files after the mapping (might require a lot of disk space)
+  -e LOG_FILE, --log-file LOG_FILE
+                        File for logging [default: stdout]
+  --sam-input SAM_INPUT Use a SAM file as input and skip read mapping step
+  --verbose             Enable DEBUG level logging
+  --save-intermediate-profile
+                        Will save abundance profile at every EM step
+  --width-filter        Apply uniform coverage filter
+```
+
+Additional flags:
+```
+  -h, --help            show usage help message and exit
+  -v, --version         show program's version number and exit
+```