Update README.md

README.md

@@ -3,7 +3,7 @@
 
 <img src="https://github.com/gersteinlab/PLIGHT/blob/main/images/PLIGHT_github_figure.png" width="600">
 
-**PLIGHT** is a computational tool that employs a population-genetics-based Hidden Markov Model of recombination and mutation to find piecewise matches of a sparse query set of SNPs to a reference genotype panel. The premise of the tool is that even limited, noisy and sparsely distributed genotypic information carries with it a certain risk of identification and downstream inference.
+**PLIGHT** is a computational tool that employs a population-genetics-based Hidden Markov Model of recombination and mutation to find piecewise matches of a sparse query set of SNPs to a reference diploid genotype panel. The premise of the tool is that even limited, noisy and sparsely distributed genotypic information carries with it a certain risk of identification and downstream inference.
 
 Inspired by imputation methods such as *IMPUTE2* [[1]](#1) and *Eagle* [[2]](#2), the inference procedure in **PLIGHT** is based on the Li-Stephens model [[3]](#3), where an HMM is used to explore the space of underlying pairs of haplotypes in a diploid genome with the possibility of de novo mutations and recombination between haplotypes. A solution to the inference problem consists of a set of best-fit haplotype pairs at each observed locus, each pair being linked to another pair at the next locus, to form a set of piecewise matches to reference haplotypes. If multiple equally likely solutions exist, the method identifies all of them.  Collectively, these form a set of genotypic trajectories through reference haplotype space, where a trajectory is defined as a sequence of reference haplotype pairs (for a diploid genome) at each locus that best fit the observations.
 
@@ -13,9 +13,15 @@ For further details about the method and application cases, please refer to:
 ## Code Description
 The code is written in Python 3, and consists of a set of three algorithms with special use cases:
 1. **PLIGHT_Exact** performs the exact HMM inference process using the Viterbi algorithm [[4]](#4);
-2. **PLIGHT_Truncated** phases in a process of truncating the set of all calculated trajectories to only those within a certain probability distance from the maximally optimal ones, resulting in a smaller memory footprint;
-3. **PLIGHT_Iterative** iteratively partitions the reference search space into more manageable blocks of haplotypes and runs **PLIGHT_Exact** on each block, followed by pooling and repetition of the scheme on the resulting, smaller cohort of haplotypes.
+    - **PLIGHT_InRef** is a specific individual-in-the-reference-database instance of **PLIGHT_Exact**, where the recombination rate is set to 0; that is, all SNPs are assumed to belong to one individual, and the most likely individual(s) in the reference database is(are) found.
+3. **PLIGHT_Truncated** phases in a process of truncating the set of all calculated trajectories to only those within a certain probability distance from the maximally optimal ones, resulting in a smaller memory footprint;
+4. **PLIGHT_Iterative** iteratively partitions the reference search space into more manageable blocks of haplotypes and runs **PLIGHT_Exact** on each block, followed by pooling and repetition of the scheme on the resulting, smaller cohort of haplotypes.
 
+The following external programs employed by the algorithms and need to be installed before running the code:
+```
+bcftools
+tabix
+```
 Some of the libraries/modules required in the corresponding Python scripts are:
 ```
 numpy (several versions work, we have used 1.18 and 1.19 at different stages of development)