Plotting and custom reference data

INSTRUCTIONS.md

@@ -1,52 +1,51 @@
 ### Running Instructions
 
-1. Upload a CSV file with the target sequences. CSV file must have ``accession_id`` and ``sequence`` columns. See [example target file](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/target.csv) which includes 5 New Omicron (``EPI_ISL_177...``) and 5 Eris (``EPI_ISL_189...``) sequences.
-
-2. Application will compare the given sequences with the average Omicron embedding. This embedding has been generated using [2000 Omicron sequences](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/omicron.csv).
-
-3. You will get a JSON response in the following format: 
-
-```json
-{
-    "EPI_ISL_18905639": {
-        "sc": 410.788391,
-        "sp": 0.000113,
-        "ip": 9e-05,
-        "log10(sc)": 2.613618,
-        "log10(sp)": -3.946409,
-        "log10(ip)": -4.047602,
-        "rank_by_sc": 2,
-        "rank_by_sp": 3,
-        "rank_by_ip": 2,
-        "rank_by_scsp": 5,
-        "rank_by_scip": 4
-    },
-    ...
-}
-```
+##### 1. Set reference dataset
 
-where:
+Application compares the target sequences with the average embedding of the reference sequences. The reference sequences can be `Omicron` or `Other`:
 
-* ``sc``: semantic change
-* ``sp``: sequence probability
-* ``ip``: inverse perplexity
-* ``rank_by_sc``: rank by semantic change (In descending order)
-* ``rank_by_sp``: rank by sequence probability (In descending order)
-* ``rank_by_ip``: rank by inverse perplexity (In descending order)
-* ``rank_by_scsp``: rank by semantic change + rank by sequence probability (Both in descending order)
-* ``rank_by_scip``: rank by semantic change + rank by inverse perplexity (Both in descending order)
+* If you select `Omicron`, application uses the average embedding of [2000 Omicron sequences](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/omicron.csv). This embedding is already generated. Thus, you do not need to upload a reference dataset.
 
+* If you select `Other`, you should upload a CSV file with the reference sequences. CSV file must have ``sequence`` column. Then, the model generates average embedding of the given sequences and uses it as a reference.
 
-### The Ranking Mechanism
+##### 2. Set target dataset
+
+Upload a CSV file with the target sequences. CSV file must have ``accession_id`` and ``sequence`` columns.
+
+See [the example target file](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/target.csv) which includes 10 Omicron (``EPI_ISL_177...``) and 10 Eris (``EPI_ISL_189...``)  sequences. It is important to note that the selected Omicron sequences are currently circulating and are not included in the training dataset.
+
+##### 3. Get output
 
-We measure grammaticality using either sequence probability (`sp`) or inverse perplexity (`ip`). Sequences that have a higher semantic change (`sc`) and higher grammaticality tend to have a higher escape potential. We rank sequences in descending order, meaning sequences with smaller rank values have a higher escape potential. In the JSON response, the sequences are sorted by ascending `rank_by_scsp`. Therefore, the top element has the smallest `rank_by_scsp`, representing the sequence with the highest escape potential.
+The output will be a dataframe with the following columns: 
+
+| Column           | Description                                                 |
+|------------------|-------------------------------------------------------------|
+| `accession_id`   | Accession ID                                                |
+| `log10(sc)`      | Log-scaled semantic change                                  |
+| `log10(sp)`      | Log-scaled sequence probability                             |
+| `log10(ip)`      | Log-scaled inverse perplexity                               |
+| `log10(gr)`      | Log-scaled grammaticality where `gr = (sp + ip) / 2`        |
+| `rank_by_sc`     | Rank by semantic change                                     |
+| `rank_by_sp`     | Rank by sequence probability                                |
+| `rank_by_ip`     | Rank by inverse perplexity                                  |
+| `rank_by_gr`     | Rank by grammaticality                                      |
+| `rank_by_scsp`   | Rank by semantic change + Rank by sequence probability      |
+| `rank_by_scip`   | Rank by semantic change + Rank by inverse perplexity        |
+| `rank_by_scgr`   | Rank by semantic change + Rank by grammaticality            |
+
+**Note:** All ranks are in descending order, with the default sorting metric being `rank_by_scgr`.
+
+See [the output](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/output.csv) for [the example target file](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/target.csv).
+
+### The Ranking Mechanism
 
-See [the response](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/response.json) for [the example target file](https://huggingface.co/spaces/smtnkc/cov-snn-app/resolve/main/target.csv).
+In the original implementation of [Constrained Semantic Change Search](https://www.science.org/doi/10.1126/science.abd7331) (CSCS), grammaticality (`gr`) is determined by sequence probability (`sp`). We propose a more robust metric for grammaticality by averaging sequence probability (`sp`) and inverse perplexity (`ip`).
 
+Sequences with both high semantic change (`sc`) and high grammaticality (`gr`) are expected to have a greater escape potential. We rank the sequences in descending order, assigning smaller rank values to those with higher escape potential. Consequently, the output is sorted based on `rank_by_scgr`, with the top element possessing the smallest `rank_by_scgr` and indicating the sequence with the highest escape potential.
 
 ### Model Details
 
-This application uses the model checkpoint with the highest zero-shot test accuracy (91.5%).
+This application uses the pre-trained model with the highest zero-shot test accuracy (91.5%).
 
 #### Training Parameters:
  
@@ -65,6 +64,7 @@ This application uses the model checkpoint with the highest zero-shot test accur
 | Margin | 2.0 |
 | Epochs | [0, 9] |
 
+To train models for specific use cases, please refer to the instructions in [our GitHub repository](https://github.com/smtnkc/CoV-SNN).
 
 #### Training Results:
 
@@ -92,6 +92,7 @@ tokenizers==0.13.3
 scanpy==1.9.3
 scikit-learn==1.2.2
 scipy==1.10.1
+plotly==5.24.1
 torch-optimizer==0.3.0
 torchmetrics==0.9.0
 torch==1.12.1+cu113

app.py

@@ -1,8 +1,9 @@
 import streamlit as st
+import os
 import pandas as pd
-import json
+import plotly.express as px
 import numpy as np
-from predict import process_target_data  # Import your function
+from predict import process_target_data, get_average_embedding  # Import your function
 
 
 st.set_page_config(page_title="CoV-SNN", page_icon="🧬")
@@ -18,23 +19,110 @@ def main():
     except FileNotFoundError:
         readme_text = "INSTRUCTIONS.md file not found."
 
-    st.markdown("Upload target sequences. Make sure the CSV file has ``accession_id`` and ``sequence`` columns.")
+    option = st.radio(
+    "Select a reference embedding:",
+    ["Omicron", "Other"],
+    captions=["Use average embedding of Omicron sequences (Pre-generated)", "Generate average embedding of your own sequences (Takes longer)"],)
+
+    # File uploader for the reference.csv
+    reference_file = st.file_uploader("Upload reference sequences. Make sure the CSV file has ``sequence`` column.",
+                                      type=["csv"], 
+                                      disabled=option == "Omicron")
 
     # File uploader for the target.csv
-    uploaded_file = st.file_uploader("Upload target sequences.", type=["csv"], label_visibility="collapsed")
+    target_file = st.file_uploader("Upload target sequences. Make sure the CSV file has ``accession_id`` and ``sequence`` columns.",
+                                   type=["csv"], 
+                                   disabled = option == "Other" and reference_file is None)
+
+    if target_file is not None and (option == "Omicron" or reference_file is not None):
+
+        if option == "Omicron":
+            # Assuming you have a pre-defined average_embedding
+            average_embedding = np.load("average_omicron_embedding.npy")
+            print(f"Average Omicron embedding loaded from file with shape {average_embedding.shape}")                
+        else:
+            with st.spinner('Calculating average embedding...'):
+                ref_df = pd.read_csv(reference_file)
+                average_embedding = get_average_embedding(ref_df)
+
+        with st.spinner('Predicting escape potentials...'):
+            # Read the uploaded CSV file into a DataFrame
+            target_dataset = pd.read_csv(target_file)
+
+            # Process the target dataset
+            results_df = process_target_data(average_embedding, target_dataset)
+
+        # Reverse the rank_sc_sp by subtracting it from the maximum rank value plus one
+        results_df['Escape Potential'] = results_df['rank_by_scgr'].max() + 1 - results_df['rank_by_scgr']
+
+        # Create scatter plot with manual color assignment
+        fig = px.scatter(
+            results_df.applymap(lambda x: round(x, 6) if isinstance(x, (int, float)) else x),
+            x="log10(gr)",
+            y="log10(sc)",
+            labels={"log10(gr)": "log10(gr)", "log10(sc)": "log10(sc)"},
+            title="CoV-SNN Results",
+            hover_name="accession_id",
+            color="Escape Potential",
+            color_continuous_scale=["green", "yellow", "red"],
+            hover_data={
+                "log10(sp)": True,     # display log10(sp)
+                "log10(sc)": True,     # display log10(sc)
+                "log10(ip)": True,     # display log10(ip)
+                "log10(gr)": True,     # display log10(gr)
+                "sp": False,            # display actual sp
+                "sc": False,            # display actual sc
+                "ip": False,            # display actual ip
+                "gr": False,            # display actual gr
+                "rank_by_sc": True,    # display rank by sc
+                "rank_by_sp": True,    # display rank by sp
+                "rank_by_ip": True,    # display rank by ip
+                "rank_by_scsp": True,  # display rank by scsp
+                "rank_by_scip": True,  # display rank by scip
+                "rank_by_scgr": True,  # display rank by scgr
+                "Escape Potential": False
+            },
+        )
+
+        # Hide the colorbar ticks and labels
+        fig.update_coloraxes(
+            colorbar=dict(
+                title=None,
+                tickvals=[],
+                ticktext=[],
+                y=0.5,
+                len=0.7
+            )
+        )
 
-    if uploaded_file is not None:
-        # Read the uploaded CSV file into a DataFrame
-        target_dataset = pd.read_csv(uploaded_file)
+        # Hide the legend
+        #fig.update_layout(showlegend=False)
 
-        # Assuming you have a pre-defined average_embedding
-        average_embedding = np.load("average_omicron_embedding.npy")
+        # add your rotated title via annotations
+        fig.update_layout(
+            margin=dict(r=110),
+            annotations=[
+                dict(
+                    text="Escape Potential",
+                    font_size=14,
+                    textangle=270,
+                    showarrow=False,
+                    xref="paper",
+                    yref="paper",
+                    x=1.14,
+                    y=0.5
+                )
+            ]
+        )
 
-        # Process the target dataset
-        results_dict = process_target_data(average_embedding, target_dataset)
+        # Display the plot in Streamlit
+        st.plotly_chart(fig, theme="streamlit", border=True, use_container_width=True, border_color="black")
 
-        # Display results as JSON
-        st.json(results_dict)
+        # Display the results as a DataFrame
+        st.dataframe(results_df[["accession_id", "log10(sc)", "log10(sp)", "log10(ip)", 
+                                 "log10(gr)", "rank_by_sc", "rank_by_sp", 
+                                 "rank_by_ip", "rank_by_gr", "rank_by_scsp", "rank_by_scip", 
+                                 "rank_by_scgr"]], hide_index=True)
 
     # Display the README.md file
     st.markdown(readme_text)

output.csv

@@ -0,0 +1,21 @@
+accession_id,sc,sp,ip,gr,log10(sc),log10(sp),log10(ip),log10(gr),rank_by_sc,rank_by_sp,rank_by_ip,rank_by_gr,rank_by_scsp,rank_by_scip,rank_by_scgr
+EPI_ISL_18986233,412.00534,0.0001191464722675543,8.76367634168547e-05,0.0001033916178422045,2.6149027,-3.9239188118774555,-4.05731367009075,-3.9855146689058785,1,1,1,1,2,2,2
+EPI_ISL_18986226,410.80124,0.00010675135854398832,8.531846659052994e-05,9.603491256725913e-05,2.6136317,-3.971626589329606,-4.068956958628795,-4.017570854680065,3,6,5,5,9,8,8
+EPI_ISL_18986234,410.80112,0.0001105443762670885,8.57568435049827e-05,9.81506098860356e-05,2.6136317,-3.956463346421271,-4.066731212651265,-4.00810699744638,4,4,4,4,8,8,8
+EPI_ISL_18986236,410.79172,0.00011333886235977685,8.645286187255717e-05,9.989586211616702e-05,2.6136217,-3.945621150908842,-4.063220625583959,-4.000452500736318,6,2,2,2,8,8,8
+EPI_ISL_18905639,410.78833,0.00011313336462511583,8.597057589875352e-05,9.955197026193468e-05,2.6136181,-3.946409296615614,-4.065650164004875,-4.001950140303386,7,3,3,3,10,10,10
+EPI_ISL_18986282,410.79742,0.0001021404935454484,8.483489298915e-05,9.348769326729921e-05,2.6136277,-3.990802047953487,-4.071425483641914,-4.029245555949711,5,7,6,7,12,11,12
+EPI_ISL_18905700,410.80365,8.802011961961398e-05,8.242524698194001e-05,8.5222683300777e-05,2.6136343,-4.0554180455651325,-4.083939742847436,-4.069444795829361,2,11,10,11,13,12,13
+EPI_ISL_18905699,410.78317,0.00010041191758079406,8.452680671922077e-05,9.246936215000741e-05,2.6136127,-3.9982147390593457,-4.0730055376304914,-4.034002138106995,9,8,7,8,17,16,17
+EPI_ISL_18905723,410.78815,9.433468566337404e-05,8.384878363173483e-05,8.909173464755444e-05,2.613618,-4.0253285933445095,-4.0765032331873785,-4.050162585115344,8,10,8,10,18,16,18
+EPI_ISL_18905548,402.63232,0.00010016525868650206,8.276867690551842e-05,9.146696779601024e-05,2.6049087,-3.999282883029627,-4.08213398713805,-4.038735717889792,10,9,9,9,19,19,19
+EPI_ISL_17781217,4.1642776,0.00010857582674361765,8.239263049415624e-05,9.548422861888695e-05,0.6195397,-3.9642668550458704,-4.084111631492836,-4.020068356054232,14,5,11,6,19,25,20
+EPI_ISL_17793474,4.1757736,8.611938045803635e-05,8.005577636043612e-05,8.308757840923624e-05,0.62073696,-4.064899103147481,-4.096607326443542,-4.08046389837658,12,13,19,15,25,31,27
+EPI_ISL_17741975,4.177282,8.326421727057701e-05,8.009380361014998e-05,8.16790104403635e-05,0.6208938,-4.079541596200184,-4.09640108144401,-4.08788953246489,11,17,18,17,28,29,28
+EPI_ISL_17793691,4.1488695,8.515223677162654e-05,8.163940861815575e-05,8.339582269489114e-05,0.61792976,-4.069803939541474,-4.088100149899155,-4.078855702671536,16,15,14,13,31,30,29
+EPI_ISL_17793453,4.1394196,8.678013742050658e-05,8.199043962649191e-05,8.438528852349925e-05,0.6169394,-4.061579666480137,-4.086236784927503,-4.073733260364415,18,12,12,12,30,30,30
+EPI_ISL_17793872,4.140045,8.529921736529407e-05,8.129646908951845e-05,8.329784322740626e-05,0.61700505,-4.069054953539316,-4.089928316499888,-4.079366243329856,17,14,16,14,31,33,31
+EPI_ISL_17793635,4.1494923,8.446570391242857e-05,8.131957528945877e-05,8.289263960094368e-05,0.61799496,-4.073319594305262,-4.089804898232796,-4.081484030639229,15,16,15,16,31,30,31
+EPI_ISL_17742593,4.1708717,7.075010398693848e-05,8.180073452905663e-05,7.627541925799757e-05,0.62022686,-4.1502729174867765,-4.087242796567261,-4.117615396521551,13,20,13,19,33,26,32
+EPI_ISL_17793695,4.1184025,7.502111548092216e-05,8.087016295337835e-05,7.794563921715025e-05,0.6147288,-4.124816482659202,-4.0922116817851215,-4.108208177035818,20,18,17,18,38,37,38
+EPI_ISL_17793481,4.1235456,7.246959033252027e-05,7.987709535991301e-05,7.617334284621663e-05,0.6152708,-4.1398441937228245,-4.097577736141062,-4.118196985094421,19,19,20,20,38,39,39

predict.py

@@ -145,20 +145,16 @@ def calculate_inverse_perplexity(sentence):
 
 
 
-
-if os.path.exists("average_omicron_embedding.npy"):
-    average_embedding = np.load("average_omicron_embedding.npy")
-    print(f"Average Omicron embedding loaded from file with shape {average_embedding.shape}")
-else:
-    omicron = pd.read_csv("omicron.csv")["sequence"].tolist()[:2000]
+def get_average_embedding(ref_df):
+    ref_sequences = ref_df["sequence"].tolist()
+    print(f"Calculating average embedding for {len(ref_sequences)} reference sequences...")
     embeddings = []
-    for i, sentence in enumerate(omicron):
+    for i, sentence in enumerate(ref_sequences):
         emb = get_sentence_embedding(sentence)
         embeddings.append(emb)
-        print(f"Embedding calculated for omicron sequence {i} with shape {emb.shape}")
+        print(f"Embedding calculated for reference sequence {i} with shape {emb.shape}")
     average_embedding = np.mean(embeddings, axis=0)
-    # Save the average embedding to a file
-    np.save("average_omicron_embedding.npy", average_embedding)
+    return average_embedding
 
 
 
@@ -206,43 +202,44 @@ def get_results_dict(target_dataset, sc_scores, sp_scores, ip_scores):
     results_df["sp"] = sp_scores
     results_df["ip"] = ip_scores
 
+    # Calculate the mean of sc and ip
+    results_df["gr"] = (results_df["sp"] + results_df["ip"]) / 2
+
     # add log10 scores
     results_df["log10(sc)"] = np.log10(results_df["sc"])
     results_df["log10(sp)"] = np.log10(results_df["sp"])
     results_df["log10(ip)"] = np.log10(results_df["ip"])
+    results_df['log10(gr)'] = np.log10(results_df['gr'])
 
     # add rank_by_sc, rank_by_sp and rank_by_ip
     results_df["rank_by_sc"] = results_df["sc"].rank(ascending=False)
     results_df["rank_by_sp"] = results_df["sp"].rank(ascending=False)
     results_df["rank_by_ip"] = results_df["ip"].rank(ascending=False)
+    results_df["rank_by_gr"] = results_df["gr"].rank(ascending=False)
 
     # make ranks integers
     results_df["rank_by_sc"] = results_df["rank_by_sc"].astype(int)
     results_df["rank_by_sp"] = results_df["rank_by_sp"].astype(int)
     results_df["rank_by_ip"] = results_df["rank_by_ip"].astype(int)
+    results_df["rank_by_gr"] = results_df["rank_by_gr"].astype(int)
 
-
-    # add rank_by_sc_sp and rank_by_sc_ip by adding the ranks of sc and sp/ip
+    # add rank_by_sc_sp, rank_by_sc_ip, and rank_by_sc_gr by adding the ranks of sc and sp/ip/gr
     results_df["rank_by_scsp"] = results_df["rank_by_sc"] + results_df["rank_by_sp"]
     results_df["rank_by_scip"] = results_df["rank_by_sc"] + results_df["rank_by_ip"]
+    results_df["rank_by_scgr"] = results_df["rank_by_sc"] + results_df["rank_by_gr"]
 
-
-    # sort by rank_by_sc_sp
-    results_df = results_df.sort_values(by="rank_by_scsp")
-
-
-    # Export the results to a JSON file
+    # Drop the sequence column
     results_df = results_df.drop(columns=["sequence"])
-    results_dict = results_df.set_index("accession_id").applymap(lambda x: round(x, 6) if isinstance(x, (int, float)) else x).to_dict(orient="index")
-    return results_dict
 
+    # Apply rounding
+    # results_df = results_df.applymap(lambda x: round(x, 6) if isinstance(x, (int, float)) else x)
+
+    # By default sort by rank_by_sc_gr
+    results_df = results_df.sort_values(by="rank_by_scgr")
+    return results_df
 
 
 def process_target_data(average_embedding, target_data):
     sc_scores, sp_scores, ip_scores = get_sc_sp_ip(average_embedding, target_data)
-    results_dict = get_results_dict(target_data, sc_scores, sp_scores, ip_scores)
-    return results_dict
-
-#out_file_name = f"response.json"
-#with open(out_file_name, "w") as f:
-#    json.dump(results_dict, f, indent=4)
+    results_df = get_results_dict(target_data, sc_scores, sp_scores, ip_scores)
+    return results_df

requirements.txt

@@ -6,6 +6,7 @@ tokenizers==0.13.3
 scanpy==1.9.3
 scikit-learn==1.2.2
 scipy==1.10.1
+plotly==5.24.1
 torch-optimizer==0.3.0
 torchmetrics==0.9.0
 torch==1.12.1+cu113

response.json

@@ -1,132 +0,0 @@
-{
-    "EPI_ISL_18905639": {
-        "sc": 410.788391,
-        "sp": 0.000113,
-        "ip": 8.6e-05,
-        "log10(sc)": 2.613618,
-        "log10(sp)": -3.946409,
-        "log10(ip)": -4.06565,
-        "rank_by_sc": 2,
-        "rank_by_sp": 3,
-        "rank_by_ip": 1,
-        "rank_by_scsp": 5,
-        "rank_by_scip": 3
-    },
-    "EPI_ISL_18905700": {
-        "sc": 410.80368,
-        "sp": 8.8e-05,
-        "ip": 8.2e-05,
-        "log10(sc)": 2.613634,
-        "log10(sp)": -4.055418,
-        "log10(ip)": -4.08394,
-        "rank_by_sc": 1,
-        "rank_by_sp": 8,
-        "rank_by_ip": 7,
-        "rank_by_scsp": 9,
-        "rank_by_scip": 8
-    },
-    "EPI_ISL_18905699": {
-        "sc": 410.783234,
-        "sp": 0.0001,
-        "ip": 8.5e-05,
-        "log10(sc)": 2.613613,
-        "log10(sp)": -3.998215,
-        "log10(ip)": -4.073006,
-        "rank_by_sc": 4,
-        "rank_by_sp": 5,
-        "rank_by_ip": 2,
-        "rank_by_scsp": 9,
-        "rank_by_scip": 6
-    },
-    "EPI_ISL_18905723": {
-        "sc": 410.788208,
-        "sp": 9.4e-05,
-        "ip": 8.4e-05,
-        "log10(sc)": 2.613618,
-        "log10(sp)": -4.025329,
-        "log10(ip)": -4.076503,
-        "rank_by_sc": 3,
-        "rank_by_sp": 7,
-        "rank_by_ip": 3,
-        "rank_by_scsp": 10,
-        "rank_by_scip": 6
-    },
-    "EPI_ISL_18905548": {
-        "sc": 402.632538,
-        "sp": 0.0001,
-        "ip": 8.3e-05,
-        "log10(sc)": 2.604909,
-        "log10(sp)": -3.999283,
-        "log10(ip)": -4.082134,
-        "rank_by_sc": 5,
-        "rank_by_sp": 6,
-        "rank_by_ip": 6,
-        "rank_by_scsp": 11,
-        "rank_by_scip": 11
-    },
-    "EPI_ISL_17742626": {
-        "sc": 4.151595,
-        "sp": 0.000121,
-        "ip": 8.3e-05,
-        "log10(sc)": 0.618215,
-        "log10(sp)": -3.916465,
-        "log10(ip)": -4.082081,
-        "rank_by_sc": 9,
-        "rank_by_sp": 2,
-        "rank_by_ip": 5,
-        "rank_by_scsp": 11,
-        "rank_by_scip": 14
-    },
-    "EPI_ISL_17742616": {
-        "sc": 4.14923,
-        "sp": 0.000128,
-        "ip": 8.3e-05,
-        "log10(sc)": 0.617968,
-        "log10(sp)": -3.891859,
-        "log10(ip)": -4.079061,
-        "rank_by_sc": 10,
-        "rank_by_sp": 1,
-        "rank_by_ip": 4,
-        "rank_by_scsp": 11,
-        "rank_by_scip": 14
-    },
-    "EPI_ISL_17781217": {
-        "sc": 4.164279,
-        "sp": 0.000109,
-        "ip": 8.2e-05,
-        "log10(sc)": 0.61954,
-        "log10(sp)": -3.964267,
-        "log10(ip)": -4.084112,
-        "rank_by_sc": 8,
-        "rank_by_sp": 4,
-        "rank_by_ip": 8,
-        "rank_by_scsp": 12,
-        "rank_by_scip": 16
-    },
-    "EPI_ISL_17741975": {
-        "sc": 4.177281,
-        "sp": 8.3e-05,
-        "ip": 8e-05,
-        "log10(sc)": 0.620894,
-        "log10(sp)": -4.079542,
-        "log10(ip)": -4.096401,
-        "rank_by_sc": 6,
-        "rank_by_sp": 9,
-        "rank_by_ip": 10,
-        "rank_by_scsp": 15,
-        "rank_by_scip": 16
-    },
-    "EPI_ISL_17742593": {
-        "sc": 4.170872,
-        "sp": 7.1e-05,
-        "ip": 8.2e-05,
-        "log10(sc)": 0.620227,
-        "log10(sp)": -4.150273,
-        "log10(ip)": -4.087243,
-        "rank_by_sc": 7,
-        "rank_by_sp": 10,
-        "rank_by_ip": 9,
-        "rank_by_scsp": 17,
-        "rank_by_scip": 16
-    }
-}

target.csv

@@ -1,11 +1,21 @@
 accession_id,sequence
 EPI_ISL_18905639,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVXYXKNNKSWMESEXRVYSSANNCTFEYVSQPFLMDLEGKXGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKPSGNYNYRYRLLRKSKLKPFERDISTEIYQAGNKPCNGVAGPNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_18905723,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDXXYQKNNKSWMESELRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKPSGNYDYRYRLLRKSKLKPFERDISTEIYQAGNKPCNGVAGPNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
-EPI_ISL_18905700,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVXYXKNNKSWMESELRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKPSGNYNYRYRLLRKSKLKPFERDISTEIYQAGNKPCNGVAGPNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDVKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986232,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFXXXXXFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYLYRLLRKSKLKPFERDISTEIYQAGNKPCNGVXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_18905548,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVXYQKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKPSGNYNYLYRFXRKSKLKPFERDISTEIYQAGNKPCNGVAGPNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENLVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_18905699,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVXYQKNNKSWMESELRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKPSGNYDYRYRLLRKSKLKPFERDISTEIYQAGNKPCNGVAGPNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
-EPI_ISL_17742626,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQSNFKNLREFVFKNIDGYFKIYSKHTPINLGRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFDEVFNATRFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGFNCYFPLRSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986236,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYQKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAXFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYLYRFLRKSKLKPFERDISTEIYQAGNKPCNGVXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986233,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYQKNNKSWMESELRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAXFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYLYRLFRKSKLKPFERDISTEIYQAGNRPCNGXXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986234,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAXFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYLYRLLRKSKLKPFERDISTEIYQAGNKPCNGVXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986282,MFVFLVLLPLVSSQCVNLITRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESELRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFXXXFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYRYRLLRKSKLKPFERDISTEIYQAGNKPCNGXXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_18986226,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTHDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPALPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKEGNFKNLREFVFKNIDGYFKIYSKHTPINLERDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPVDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFHEVFNATTFASVYAWNRKRISNCVADYSVIYNFAXFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNKLDSKXXGNYNYLYRLLRKSKLKPFERDISTEIYQAGNKPCNGVXXXNCYSPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_17781217,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAISGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLGRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFDEVFNATTFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVGGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGVNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_17741975,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHNNNKSWTESEFRVYSSAKNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLGRDLPQGFSALEPLVDLPIGINITRFQTLLALNRSYLTPGDSSSDWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFDEVFNATTFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSRVSGNYDYMYRLFRKSKLKPFERDISTEIYQAGNKPCNGVRGSNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
 EPI_ISL_17742593,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLGRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFDEVFNATRFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGFNCYFPLRSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
-EPI_ISL_17742616,MFVFLVLLPLVSSQCVNLITRTQSYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLGRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFDEVFNATRFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGFNCYFPLRSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTHTKSHRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKLALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELHSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793453,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWVFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVSGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793481,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVSGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGXXXXXXXQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793474,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQVNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSSWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVSGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYXXQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793872,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPXXXXXXXXXITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEXXIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVSGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLFSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793635,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFIGCVIAWNSNKLDSKVSGNYNYMYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793691,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKVSGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPALEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT
+EPI_ISL_17793695,MFVFLVLLPLVSSQCVNLITRTQLSPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLDVYHKNNKSWMESGVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTLINSVRHLPQGFSVLEPLVDLPIGINITRFQTLLHRSYLTPGDSSLGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATSFASVYAWNRKRISNCVADYSVLYNFAPFFAFKCYGVSPTKLNDLCFTNVYADSFVIRGNEVSQIAPGQTGNIADYNYKLPDDFTGCVIAWNSNKLDSKASGNYNYRYRLFRKSNLKPFERDISTEIYQAGNKPCNGVAGPNCYFPLQSYGFRPTYGVGHQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEYVNNSYECDIPIGAGICASYQTQTKSHRRARSVASQSIIAYTMSLGAEISVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLKRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKYFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNHNAQALNTLVKQLSSKFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT